Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/springer-journals/indirect-epigenetic-testing-identifies-a-diagnostic-signature-of-AzNKEObA0Q
- Publisher
- Springer Journals
- Copyright
- Copyright © The Author(s) 2023
- ISSN
- 0300-8428
- eISSN
- 1435-1803
- DOI
- 10.1007/s00395-022-00954-3
- Publisher site
- See Article on Publisher Site
Abstract
Precision-based molecular phenotyping of heart failure must overcome limited access to cardiac tissue. Although epigenetic alterations have been found to underlie pathological cardiac gene dysregulation, the clinical utility of myocardial epigenomics remains narrow owing to limited clinical access to tissue. Therefore, the current study determined whether patient plasma confers indirect phenotypic, transcriptional, and/or epigenetic alterations to ex vivo cardiomyocytes to mirror the failing human myocardium. Neonatal rat ventricular myocytes (NRVMs) and single-origin human induced pluripotent stem cell- derived cardiomyocytes (hiPSC-CMs) and were treated with blood plasma samples from patients with dilated cardiomyopathy (DCM) and donor subjects lacking history of cardiovascular disease. Following plasma treatments, NRVMs and hiPSC-CMs underwent significant hypertrophy relative to non-failing controls, as determined via automated high-content screening. Array-based DNA methylation analysis of plasma-treated hiPSC-CMs and cardiac biopsies uncovered robust, and conserved, alterations in cardiac DNA methylation, from which 100 sites were validated using an independent cohort. Among the CpG sites identified, hypo-methylation of the ATG promoter was identified as a diagnostic marker of HF, wherein cg03800765 methylation (AUC = 0.986, P < 0.0001) was found to out-perform circulating NT-proBNP levels in differentiating heart failure. Taken together, these findings support a novel approach of indirect epigenetic testing in human HF. Keywords Precision medicine · Epigenetics · Heart failure · DNA methylation · Pilot study Abbreviations Christian U. Oeing, Mark E. Pepin, Dominik Siede, and Johannes DCM Dilated cardiomyopathy Backs have contributed equally to this work. DMP Differentially methylated position DEG Differentially expressed gene * Johannes Backs HF Heart failure Johannes.backs@med.uni-heidelberg.de hiPSC-CMs Human-induced pluripotent stem cell Institute of Experimental Cardiology, University Hospital derived cardiomyocytes Heidelberg, University of Heidelberg and DZHK NRVMs Neonatal rat ventricular myocytes (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Im Neuenheimer Feld 669, 69120 Heidelberg, Germany Introduction Department of Internal Medicine and Cardiology, Charité University Medicine, DZHK (German Center for Cardiovascular Research), Partner site Berlin, Campus Heart failure (HF) is a multifaceted clinical syndrome that Virchow-Klinikum, Berlin, Germany is diagnosed based on clinical evidence of hemodynamic Cancer Epigenomics, German Cancer Research Centre insufficiency. Patients with HF initially present with non- (DKFZ), Heidelberg, Germany specific symptoms of fatigue and exertional dyspnea, war - Department of Cardiology, University of Heidelberg, DZHK ranting a broad diagnostic workup to identify the underlying (German Centre for Cardiovascular Research), Partner Site cause(s). Despite its widespread use, the poor specificity Heidelberg/Mannheim, Heidelberg, Germany of elevated circulating BNP or NT-proBNP levels limits its Institute of Pharmacology and Toxicology, Technische use as a diagnostic tool to “ruling-out” the presence of HF Universität Medical Centre Dresden, Dresden, Germany Vol.:(0123456789) 1 3 9 Page 2 of 16 Basic Research in Cardiology (2023) 118:9 [54]. Techniques to characterize the functional consequences [11, 13, 28, 35], displaying both etiology-specific [36] and of cardiac dysfunction, including non-invasive imaging and socioeconomically driven [37] effects on cardiac metabolic functional tests, provide some prognostic insights, but no programs. Hence, DNA methylation may encode the com- molecular tests are yet available to diagnose HF. A new plex environmental exposures, including circulatory milieu, approach to diagnose HF and predict outcome is therefore which lead to cardiac dysfunction. needed, one which reflects the molecular foundations of its Therefore, the current study employs a novel diagnos- pathogenesis. tic approach via indirect epigenetic testing to determine Although lifestyle and genetic factors have been shown to whether circulating factors are capable of driving epigenetic confer HF risk, their convergence onto epigenetic machin- reprogramming of cardiomyocytes. The current study treated ery presents an opportunity for diagnostic testing. Genome- human inducible pluripotent stem cell-derived cardiomyo- wide association studies have uncovered thousands of causal cytes (hiPSC-CMs) with plasma collected from patients with genetic mutations [4], but the clinical value of these discov- non-ischemic HF caused by dilated cardiomyopathy (DCM, eries is limited by both the relative infrequency and pleiot- n = 13) and healthy donors (n = 10) (Fig. 1). Genome-wide ropy of monogenic cardiomyopathies [25]. Environmental analysis of array-based CpG methylation identified 49 “indi- and behavioral factors such as obesity [1], diabetes mellitus rect” epigenomic markers of DCM, which were validated in [18, 19], and hypertension [39] are far more prevalent risk a larger published cohort. Therefore, we offer preliminary factors for HF, though the synergistic effects of environmen- evidence to support the feasibility of indirect epigenetic test- tal exposures and the plethora of mediators remain largely ing of DCM using hiPSC-CMs. unknown. Recent studies have therefore begun to study the molecular basis of gene-environment or epigenetic interac- tions as underlying determinants of HF susceptibility and Methods pathogenesis [42]. Unlike the direct epigenetic profiling of solid tumors, Ethics statement which has already shown promise in precision-based oncol- ogy [52], diagnostic access to myocardial tissue remains Human studies were approved by the ethics committee and comparably limited. Epigenetic modifications, whether medical faculty at the Heidelberg University Hospital (Hei- directly to DNA via CpG methylation or to ancillary struc- delberg, Germany; appl. no. S-390/2011). Informed consent tures including histone proteins, have been linked to patho- was obtained for the procurement of left ventricular assist genesis of cardiovascular disease [12, 20, 26, 44, 49, 53]. device core biopsies, and a waiver of consent was granted Recent studies have uncovered robust differences in cardiac for tissue samples received from non-failing hearts of organ DNA methylation in patients with end-stage heart failure donors. Control blood samples were obtained according to Fig. 1 Graphical overview. Human inducible pluripotent stem cells sis with the Illumina Beadchip HumanMethylation450k (m450k) (iPSC-CMs) were treated with plasma from either DCM (n = 13) or Array platform. Data were then cleaned and analyzed in comparison healthy (n = 10) subjects for 48 h. Samples were then analyzed for to m450k analysis of human cardiac biopsies from explanted hearts of cell size using InCell Analyzer and submitted for methylation analy- DCM patients (n = 7) and non-failing donor controls (n = 3) 1 3 Basic Research in Cardiology (2023) 118:9 Page 3 of 16 9 the protected health information 45 C.F.R. 164.514 e2 (Bios- hiPSC-CM were fixed, washed and were incubated with the erve) and the BCI informed consent F-641-5 (Biochain). primary antibody (Troponin T, Cardiac Isoform Ab-1 (Clone Patient health information was acquired at time of tissue 13–11)) (Thermo Fischer Scientific; MS-295-P1) over night acquisition, and all human RNA-sequencing and DNA meth- and incubated with the secondary antibody (Alexa 488 Goat ylation array data are available upon request. anti- Ms. IgG1; Thermo Fisher Scientific A21121). Negative control is missing the first antibody (Troponin T) to show Patient samples specificity of antibody binding. Quantification is performed using an automated high-throughput algorithm with InCell All samples were obtained from and authorized by the microscope (Supplemental Fig. S1C). Heidelberg University Hospital Biobank (Heidelberg, Ger- many). Biopsies were selected according to age and gen- Isolation of neonatal rat ventricular cardiomyocytes der matching with reduced systolic left ventricular ejection (NRVMs) fraction (LVEF) and dilatation (Supplemental Table 1). Exclusion criteria included evidence of coronary artery dis- Heart pieces of 1- to 2-day-old Wistar rats were digested ease or other clinically relevant cardiac conditions. Human by a mix of collagenase (CellSystems Biotechnologie Ver- myocardial biopsies were obtained from patients with DCM triebs GmbH) and pancreatin (Sigma-Aldrich) and incu- (n = 7) or from non-failing donor hearts (n = 3), as described bated at 37 °C for 20 min. The supernatant containing the previously [41]. NRVMs was sequentially collected. NRVMs were pelleted by centrifugation and re-suspended in a salt balanced solu- Differentiation of human induced pluripotent stem tion. NRVMs were finally purified using a discontinuous cells into cardiomyocytes Percoll gradient (GE Healthcare). Cells were re-suspended in DMEM (Sigma-Aldrich) with supplements and plated on To determine whether cardiomyocytes exhibit differences collagen (Sigma-Aldrich) coated cell culture plates (Greiner in DNA methylation in vitro, hiPSC-CMs were differenti- Bio-One) [40]. ated using an established protocol [29, 41]. Briefly, hiPSCs were harvested from Matrigel (BD Bioscience; 354,277) Cardiomyocyte plasma treatments coated 6-well plates (Corning) and cultured with Essen- tial 8 medium (Thermo Fisher Scientific; A1517001) For cell size and perinuclear atrial natriuretic peptide and ROCK inhibitor (Tocris; 1254). The hiPSCs were cul- (ANP) staining measurements, hiPSC-CMs were plated in tured for 3 days or until achieving a confluence of 70–90%. octuplets on 96-well black µClear plates (Greiner Bio-One) The medium was then replaced by RPMI1640 (Thermo with Matrigel (BD Bioscience) coating and NRVMs were Fisher Scientific; 21875-034), insulin-free B27 Supple- plated on collagen. For DNA isolation, cells were plated on ment (Thermo Fisher Scientific; A1895601) and 10 μM 12-well plates. After 24-h starvation with FCS-free medium, CHIR99021 (Tocris; 4423) for 24 h. The next day (Day 1), NRVMs and hiPSC-CMs were treated for 48 h with 5% the medium was changed to RPMI1640 and insulin-free patient plasma from DCM or non-failing control (CON) B27 Supplement. 24 h later (Day 2), cells were treated with subjects instead, or with fetal calve serum (FCS) or FCS- 5 μM IWP2 (Tocris, 3533) in RPMI1640 with B27 Sup- free medium (“starve”). plement minus insulin. On Day 5, the medium was again changed to RPMI1640 plus insulin-free B27 Supplement. Cardiomyocyte immunofluorescence staining After Day 7 the medium was changed every two days with RPMI1640 with B27 Supplement (Thermo Fisher Scientific; Cardiomyocytes were fixed with paraformaldehyde (Sigma- 17,504,044) until day 15. To enrich cardiomyocytes, meta- Aldrich) after 48-h treatment. Antibodies against cardiac bolic stress was induced using 4 mM lactate as described by α-actinin (Sigma-Aldrich) and ANP (Peninsula Lab) were Tohyama et al. [48]. used sequentially overnight at 4 °C. Secondary antibodies Quality of isolation, and purity of hiPSC-CMs were (Thermo Fisher Scientific) were incubated for 1 h at room assessed using cardiac troponin (cTNT) positivity versus temperature. Nuclei were stained with DAPI (Thermo negative control after maturation (Supplemental Fig. S1A) Fisher Scientific). Histological imaging and analyses were and after plasma treatment (Supplemental Fig. S1B). Briey fl , performed using an InCell Analyzer 2200 (GE Healthcare), 1 3 9 Page 4 of 16 Basic Research in Cardiology (2023) 118:9 where cell size and perinuclear ANP intensity could be sequences were trimmed from reads files using trimgalore measured using the automated HTS approach, which has (0.5.0). been developed and validated by the InCell investigator soft- ware (GE Healthcare). Cell sorting results for troponin is Bioinformatics shown in Supplemental Fig. 1A. As a proxy of stable purity after treatment of hiPSC-CMs, viable cells were quantified All coding scripts used in the current study are available as using the same HTS approach by counting all DAPI + cells an online supplement via GitHub data repository: https:// and actinin overlay (see Supplemental Fig. 1B–C). Repro- git hub. com/ mepep in/ Indir ect. Epig e nomics. Differential ducibility of cell size measurements in different hiPSC-CM methylation analysis was performed as previously described cell lines is shown in Supplemental Fig. 2A. [36]. Differential methylation analysis was completed by fit- ting probe-wise linear models to the normalized log-ratios, HumanMethylation450k BeadChip (m450k) Array followed by an empirical Bayesian shrinkage of probe-wise sample variance via Minfi (1.40.0) within the R (4.1.2) sta- Epigenome-wide DNA methylation studies were performed tistical computing environment [43]. using the Illumina Beadchip HumanMethylation450k For RNA-sequencing analysis, alignment of reads to the (m450k) array platform, as previously described [36]. For hg19 genome was accomplished using STAR (v2.7.9a), each assay, 500 ng DNA was bisulfite-treated before amplifi- yielding ~ 95% uniquely mapped reads for all samples. Raw cation, hybridization, and imaging standard to the Illumina counts were generated using Samtools [21], with differen- protocol. Briefly, frozen biopsies were disrupted using the tial gene expression performed using DESeq2 [22] (1.34.0) TissueRuptor (Qiagen). DNA isolation of disrupted biop- within the R (4.1.2) computing environment [38]. Dispersion sies or pelleted NRVMs and hiPSC-CMs was done using the estimates were determined via maximum-likelihood, which QIAamp DNA Blood and Tissue Kit (Qiagen) according to were shrunken according to an empirical Bayes approach the manufacturer’s protocol. DNA integrity was monitored to provide normalized count data for genes proportional to by gel electrophoresis. Array intensity data generated via both the dispersion and sample size. Differential expres- iScan were preprocessed and normalized using quantile sion was then determined from normalized read counts normalization to adjust for technical differences in Type I/II via Log (fold-change) using the Wald test followed by array designs [23]. Total (methylated + unmethylated) signal Bonferroni-adjusted P value for each aligned and annotated intensity for each probe was weighed against the background gene. From this, 2077 genes were found to be differentially signal via negative control probes to provide a statistical (P expressed at P < 0.05. value) detection threshold (Supplemental Fig. S3). Possi- ble confounding of differential methylation via overlapping Statistical analysis SNPs was evaluated using MethylToSNP (0.99.0), removing 1494 CpG probes from the analysis of cardiac biopsy sam- For all pairwise comparisons, the Shapiro–Wilk test for ples (Supplemental Fig. S4); no SNPs were detected among normality was performed to determine the most appropriate iPSC-CMs. statistical test. Statistical comparisons were achieved using two-tailed t tests between DCM and CON in the cell size RNA‑sequencing and ANP intensity as well as qPCR experiments. All data are reported as mean ± standard deviation unless otherwise RNA sequencing analysis was performed as previously specified. outlined [36], with detailed methods available as an online supplement. Briefly, RNA was isolated from iPSC-CMs using Qiazole™ reagent (Qiagen Inc., Hilden, Germany) and validated via fragment analysis (Agilent) to ensure Results RNA quality. Sample B2 was removed (RIN = 2.5) and was identified owing to RNA Integrity Numbers (RINs) which DCM patients’ plasma increases cardiomyocyte size were 9.2 ± 1.5, with all samples achieving RINs > 7 (Sup- and perinuclear ANP plemental Table 2). Samples were then submitted for paired- end 100 bp RNA sequencing which was performed at BGI To determine whether 48-h exposure to human plasma Tech Solutions (Hong Kong, CN), where high-throughput impacts cardiomyocyte morphology in accordance with next-generation RNA-sequencing was performed using the the patients’ diagnosis of HF, cell size was quantified using ™ ™ DNBSEQ G400 platform. Prior to alignment, adapters the InCell automated high-content screening (HTS) assay and low-quality (PHRED < 20, or 1% sequencing error rate) 1 3 Basic Research in Cardiology (2023) 118:9 Page 5 of 16 9 Fig. 2 DCM patients’ plasma increases cardiomyocyte size. After DAPI (n = 4). Starvation vs. FCS is represented as a mean value of 48 h of treatment with 5% plasma from dilated cardiomyopathy each well count with each approximately 1300 cells counted per (DCM, n = 13) or healthy control (CON, n = 10) subjects, cell size well. In contrast, CTR vs. DCM is represented as a mean value of was measured for A NRVMs and B hiPSC-CMs. C Representa- octuplets with each well counting approximately 1300 cells, hence a tive immunocytochemistry-based quantification of atrial natriuretic mean of a mean of 8 wells (a mean of 8 means, derived from approx. peptide (ANP) performed in DCM plasma-treated (DCM) relative 1300 cells each). Student’s t-test reporting mean ± S.E.M. (*P < 0.05, to control plasma-treated hiPSC-CMs co-stained for α-Actinin and **P < 0.01, ***P < 0.001) 1 3 9 Page 6 of 16 Basic Research in Cardiology (2023) 118:9 for NRVMs (Fig. 2A) and iPSC-CMs (Fig. 2B). In both DNA methylation changes detected in the indirect NRVMs and hiPSC-CMs, exposure to plasma from DCM cardiomyocyte test patients conferred a 22% (P = 0.004) and 27% (P < 0.001) increase in cell size, respectively. Cardiomyocyte hypertro- To determine whether circulating factors are sufficient to phy was reproducible, seen in repeated experiments with trigger alterations in cardiac DNA methylation reminis- hiPSC-CMs from two additional independent cell lines cent of failing hearts, hiPSC-CMs were exposed to plasma (Suppl. Figure 2A). To determine whether exposure to obtained from patients with DCM or age-matched healthy plasma from DCM patients could reproduce pathological control (CON) subjects. Unlike in cardiac biopsies, unsu- hallmarks of cardiac stress, an HTS approach was used to pervised clustering failed to differentiate between iPSCs quantify both ANP abundance and its subcellular distribu- exposed to DCM plasma (n = 13) and those with CON tion within hiPSC-CMs. Immunohistochemical staining plasma (n = 10) (Fig. 4A). Nevertheless, a robust signature demonstrated greater abundance of perinuclear ANP stain- of differential methylation was seen between DCM and CON ing in the hiPSC-CMs treated with DCM plasma relative plasma treated hiPSC-CMs, with 28,381 DMPs (P < 0.05) to CON plasma (Fig. 2D), though neither ANP abundance detected. Of these, five DMPs achieved genome-wide signif- –6 nor cell size correlated with circulating NT-proBNP levels icance (Fig. 4B): cg03800765 (ATG7, 32.4%, P = 8.6 × 10 ), –6 (Suppl. Figure 2B–C). cg14156314 (C9orf140, – 0.7%, P = 4.1 × 10 ), cg18502522 –6 (SCAMP2, – 24.5%, 2.2 × 10 ), cg07561469 (CCNF, –6 DNA methylation changes in cardiac biopsies – 31.1%, P = 1.2 × 10 ), and cg05274755 (NPAS3, – 19.0%, –7 P = 1.3 × 10 ). Furthermore, the highest proportion of The Illumina Beadchip HumanMethylation450k array DMPs relative to the m450k array were associated with was used to quantify CpG methylation intensity of DNA promoter-associated CGIs, stressing a potential regulatory isolated from biopsies of DCM (n = 7) and non-failing con- influence on adjacent coding regions (Fig. 4C). Among trol hearts (CON, n = 3). Unsupervised multi-dimensional the CGI-associated DMPs, most were found within the scaling (MDS) of the 10,000 most-variable CpG probes promoter of adjacent coding regions (Fig. 4D), although revealed a marked separation in cardiac DNA methylation robust differences in methylation were seen across genomic signature between DCM and CON samples (Fig. 3A). Dif- regions, as visualized via heatmap and hierarchical cluster- ferential quantification of DCM and CON identified 84,024 ing (Fig. 4E). Taken together, these observations support differentially methylated CpG sites (DMPs) (P < 0.05), that, although a global shift in DNA methylation does not with the most robust alterations seen in cg02459042 (NXN, distinguish between hiPSC-CMs treated with DCM versus –8 63.6% hyper-methylated, P = 1.3 × 10 ) (Fig. 3B). Because CON plasma, robust alterations in DNA methylation still DNA methylation is known to regulate gene expression occur within promoter-associated CGIs. in a site-dependent manner [3, 17], DMP distribution was performed according to where plotted onto both annotated Common epigenetic changes detected in cardiac gene regions (promoter, 5’UTR, gene body, and 3’UTR) as biopsies and by the indirect approach well as according to their distance from CpG Islands (CGIs) (Fig. 3C); the resulting distribution revealed that, although To identify “indirect” epigenetic loci in plasma-treated the greatest overall number of DMPs were located within iPSC-CMs, we compared DMPs found in both myocardial gene bodies, a disproportionate percentage of DMPs were and iPSC-CM analyses (Fig. 5A). Albeit a minority of co- found within "North Shore”-associated CpG sites within the methylated CpG sites, 389 concordant DMCs (coDMCs) proximal promoter of adjacent genes (Fig. 3C–D). Neverthe- associated with 426 genes were found between cardiac biop- less, strong heart failure-associated signatures of differential sies and iPSC-CMs. Gene set enrichment revealed dispro- methylation were seen throughout the annotated genomic portionate differential methylation proximal to genes associ- regions (Fig. 3E). Taken together, these findings support pre- ated with “Apoptosis” (P = 0.007, 9 DMCs), “Myogenesis” viously published evidence of robust epigenomic shifting in (P = 0.01, 10 DMCs), “Epithelial-Mesenchymal Transition” end-stage human heart failure [13, 28, 35–37]. (P = 0.01, 10 DMCs), and “Heme Metabolism” (P = 0.01, 10 DMCs) pathways (Fig. 5B). 1 3 Basic Research in Cardiology (2023) 118:9 Page 7 of 16 9 A B C D Methylation (Z-score) Group DCM CON Fig. 3 Cardiac DNA methylation in cardiac biopsies. A Multidimen- and |methylation %|> 5 highlighted in yellow. Labelled are the 10 sional scaling (MDS) of top-10,000 CpG probes within the Illumina most-robustly hyper-methylated and hypo-methylated CpG probes by HumanMethylation450k array performed on cardiac left ventricle % methylation. C Distribution of differential methylation via three- samples from patients with end-stage heart failure (DCM) or non-fail- dimensional contour plot of differentially methylated CpG probes ing donor control hearts (CON). The two principal components that (DMPs)* categorized according to their presence within genomic account from the largest variance in DNA methylation were used to (Promoter, 5’ UTR, Body, Exon–Intron boundary, or 3’ UTR) and generate a scatterplot, flanked by density plots of each principal com- CpG (Shelf, Shore, and Island) regions. Bar graph depicting the num- ponent. B Volcano plot illustrating the robustness of CpG methylation ber of DMPs within each genomic region. D proportional distribution differences, plotting (– log [P value]) as a function of percent differ - of CpG Island-associated DMPs. E Heatmap and hierarchical cluster- ence in methylation (%) in DCM vs. CON, probes exceeding P < 0.05 ing of DMPs according to each genomic region. *P < 0.05 1 3 V 9 Page 8 of 16 Basic Research in Cardiology (2023) 118:9 A B C10 C2 B3 A4 C8 C6 C3 C4 C9 C5 A6 A5 A7 B4 B5 B6 B1 B7 C7 A1 A2 −50 C1 A3 B2 −50 050100 Principal Component 1 C D Methylation E (Z-score) Plasma Origin DCM CON Fig. 4 DNA methylation changes detected in the indirect cardiomyo- methylated CpG probes by % methylation. C Distribution of differ - cyte test. A MDS plot of top-10,000 CpG probes within the Illumina ential methylation via three-dimensional contour plot of differentially HumanMethylation450k array performed on inducible pluripotent methylated CpG probes (DMPs)* categorized according to their pres- stem cell (iPSC)-derived cardiomyocytes exposed to plasma from ence within genomic (Promoter, 5’ UTR, Body, Exon–Intron bound- patients with end-stage heart failure (DCM; n = 13) relative to plasma ary, or 3’ UTR) and CpG (Shelf, Shore, and Island) regions. Bar from healthy (CON; n = 10) patients. B Volcano plot illustrating the graph depicting the number of DMPs within each genomic region. robustness of CpG methylation differences, plotting (- log [P value]) D proportional distribution of CpG Island-associated DMPs. E Heat- as a function of percent difference in methylation (%) in DCM vs. map and hierarchical clustering of DMPs according to each genomic CON, probes P < 0.05 and |methylation %|> 5 are highlighted in yel- region. *DMPs defined via P < 0.05 low. Labelled are the 10 most-robustly hyper-methylated and hypo- 1 3 Principal Component 2 Basic Research in Cardiology (2023) 118:9 Page 9 of 16 9 To validate DNA methylation differences observed in (AUC = 0.986, P < 0.0001) methylation relative to circu- our cohort of human cardiac biopsies, the overlapping 389 lating cells (AUC = 0.789, P < 0.0001), iPSC-CM mRNA coDMCs were compared those of a testing cohort of car- (AUC = 0.639, P = 0.264), and circulating NT-proBNP diac and blood samples from DCM (n = 41) and non-failing levels (AUC = 0.75, P = 0.05). (n = 31) control subjects from Meder et al. [28] (Fig. 5C); To identify putative upstream signaling that could be 100 DMCs were validated in cardiac biopsies (25.7% over- impacted by ATG7 methylation at cg03800765, motif lap, P < 0.043), and 115 DMCs were also seen in blood enrichment was performed using the MEME suite for CpG (29.6%, P < 0.01). Examination of the top 5 most robustly site-specific motif discovery at this DMC locus (± 10 BP). differentially methylated CpGs in iPSC-CMs that were This approach identified CREB1 as a likely upstream tran- validated uncovered CpG island-associated CpGs located scriptional regulator (Fig. 6C), consistent with published at – or near – the promoter regions for ATG7 (cg03800765, evidence [32]. Downstream scanning of all DMCs for –6 – 32.4%, P = 9.0 × 10 ), DZIP1L (cg09151521, 30.7%, CREB1 response elements in DCM plasma-treated iPSC- P = 0.007), ZNF397OS (cg26141063, – 29.3%, P = 0.005), CMs identified 117 overlapping DMCs; of these, 46 (39%) TGFBR3 (cg17074213, – 28.4%, P = 0.004), and POL2A were located within the proximal promoter of adjacent genes (cg21257117, 25%, P = 0.005) (Fig. 5D). Plotting of each (Fig. 6D). Taken together, these observations suggest that DMC revealed equivalent degrees of differential methyla- epigenetic competition of CREB1 binding may influence tion at these sites between cardiac biopsies and iPSC-CMs ATG7 expression in DCM. (Fig. 5E). To determine whether any of these CpG sites of iPSC- CMs are associated with differences in transcriptional Discussion activity, next-generation RNA-sequencing analysis was performed on the samples submitted for DNA methylation As a molecular readout for gene-environment interactions, analysis. Among the 2,077 differentially expressed genes epigenomic profiling offers potential for precision-based (DEGs), 49 were accompanied by proximal differential clinical diagnostics [7, 9, 24, 47, 52, 56]. For conditions methylation (Table 1, Fig. 5C). Therefore, although the in which tissue is difficult to access, including cardiovas- exposure of hiPSC-CMs to human plasma does not com- cular and neurologic diseases, clinical decision-making is prehensively recapitulate the transcriptional alterations seen forced to rely on indirect measurements, though no epige- in the failing myocardium, the indirect measurement of CpG netic biomarkers have yet been identified for diagnostic or methylation permits a differentiation between DCM and prognostic purposes. Myocardial epigenetics has mostly CON biopsies and impacts pathways known to contribute been studied using biopsies from end-stage failing or to cardiac dysfunction. post-mortem “healthy” hearts [5, 14, 31, 49, 51], thereby missing the early stages of HF in which manifestations of ATG7 as a putative epigenetic biomarker of DCM cardiac dysfunction may be reversible. In this study, we in iPSC‑CMs demonstrate the usefulness of routinely acquired blood plasma to circumvent these problems via indirect epige- To better understand the transcriptional potential of netic testing of DCM patients. single-site CpG methylation on associated gene expres- sion, the most robustly differentially methylated CpG was Indirect model of epigenetic testing taken as a use-case scenario (Fig. 6A), which displayed a strong correlation (spearman ρ = 0.61, P = 0.0026) Although genetic heterogeneity is known to confound DNA between methylation at cg03800765 and expression of the methylation analyses, the hiPSC-CMs used in this study adjacent gene ATG7. Area under the receiver operating were generated from a single healthy adult of European characteristics (ROC) curves (AUCs) were computed for ancestry, thereby circumventing genetic confounding. Treat- cg03800765 methylation intensity or ATG7 expression for ment of iPSC-CMs with patient plasma induced both cel- each dataset (Fig. 6B), revealing markedly higher AUCs lular hypertrophy and perinuclear ANP accumulation, both for cardiac biopsy (AUC = 1.0, P = 0.0167) and iPSC-CM of which reflect properties of failing myocardium. Similarly, 1 3 9 Page 10 of 16 Basic Research in Cardiology (2023) 118:9 A B TSHZ3 GALK2 CON HAVCR2 DCM KANK2 NID2 NT5DC1 Methylation MAN2B1 SYTL2 (Z−score) SCN7A TGFBR3 SLC40A1 CTF1 1 RBBP7 MLLT3 0 VEZT VANGL2 −1 LRP1 ADPRHL1 −2 ADAMTS2 RGS12 MAP4K2 CpG COL5A1 PPP1R9A S_Shelf QSOX1 ARHGEF3 S_Shore APP Island ASAP3 N_Shore MAST1 HECW2 N_Shelf OpenSea Location ZCCHC24 TSS1500 TSS200 MYO18A DHTKD1 5'UTR GCNT2 1stExon TRIM9 TPPP Body RPS12 CSRP1 3'UTR UNC45B TNS1 KCNJ2 iPSC DEGs GNG7 GALK2 KCNMB4 ALG2 HSD17B8 SLC12A7 −1 DLEU2 ITGA1 −2 GABBR1 ATG7 DZIP1L ZNF271 TGFBR3 POLA2 2.0 1.5 1.0 0.5 1 3 Methylation (normalized beta) Basic Research in Cardiology (2023) 118:9 Page 11 of 16 9 ◂Fig. 5 Concordant epigenetic signature of iPSC-CMs and cardiac of cardiomyocyte epigenetics may permit a collective assess- biopsies. A Hierarchical clustering and heatmap visualization of ment of these factors and potentially influence myocardial 389 concordantly methylated DMPs (coDMPs)* in both cardiac tis- disease fate. Therefore, we hypothesize that the measure- sue (red) and iPSCs (blue) treated with plasma from DCM (cyan) ment of epigenetic consequences may be superior in predict- or healthy (grey) subjects. RNA-sequencing log Fold-Change plot- ted alongside DNA methylation B Gene-set enrichment analysis of ing cardiovascular disease. the 426 proximal genes associated with at least one of the coDMCs, using the KEGG 2020 molecular signatures database with statistical enrichment calculated using enrichR. C Venn diagram illustrating the DNA methylation as a proxy of HF diagnosis shared DMCs between the 389 coDMPs, m450k analysis of cardiac biopsies for DCM vs. CON (n = 41), and m450k analysis of buffy and outcome coat for DCM vs. CON (n = 31). D Top 5 most differentially-methyl- ated CpG sites in iPSC-CMs that could be validated using the Meder Our analysis uncovered robust differential methylation et al. dataset. E bar plot of the top 5 most robust DMCs that were pre- –6 cg03800765 in both iPSC-CMs (– 32.4%, P = 9.0 × 10 ) and sent in the validation datasets. Each dot represents methylation levels of 1 well of approx. 1 million hiPSC-CMs treated with plasma, or of cardiac biopsies (– 25.2%, P = 0.004), a CpG site located the available amount of myocardial tissue from patients. *P < 0.01 within a promoter-associated CpG island upstream of ATG7. Although methylation at this site was negatively correlated DNA methylation analysis identified 389 concordant DMPs with ATG7 expression (P = 0.0026), only cg03800765 (Fig. 5A), enriching pathways known to be disrupted in methylation was significantly predictive of patient diag- HF (Fig. 5B); among these, 100 DMPs (25.7%) were vali- nosis with HF in iPSC-CMs (P < 0.0001), cardiac biopsies dated in a larger independent cohort of DCM (n = 41) [28]. (P = 0.0167), and circulating cells (P < 0.0001); by contrast, Although we identify many promising candidates (Table 1), ATG7 expression failed to provide any diagnostic benefit cg03800765 methylation exhibited superior diagnostic per- (P = 0.264). Furthermore, cg03800765 methylation in iPSC- formance to both circulating NT-proBNP levels and ATG7 CMs out-performed circulating NT-proBNP levels as a expression in our cohort (Fig. 6B). Therefore, although diagnostic marker, underscoring its potential usefulness via future studies are needed to establish its clinical usefulness, indirect epigenetic testing (Fig. 6B). Although larger clini- we provide the conceptual basis for indirect epigenetic test- cal cohorts are needed to evaluate the potential of indirect ing in HF. epigenetics to predict HF risk, cg03800765 is a promising candidate. Circulating factors in heart failure Autophagy and ATG7 Despite the robust phenotypic and epigenetic consequences The genomic region adjacent to cg03800765 encodes the that were observed following plasma treatments, it remains ubiquitin-like modifier-activating enzyme ATG7 , a pro- unknown which circulating factor(s) is/are ultimately tein involved in phagolysosome formation and mitophagy responsible. Their identification could enable direct meas- [6]. Autophagy is essential to maintaining the regenerative urement of plasma; however, we hypothesize that cardio- potential of hematopoietic progenitor cells, and controls myocyte phenotype is dictated by a circulatory milieu that metabolic activity via epigenetic regulation, the dysregula- converges onto epigenetic machinery. Cytokines have been tion of which leads to heart failure [15, 33, 34]. Although found to predict cardiac functional improvement on mechan- no studies have yet explored the consequences of disrupted ical circulatory support [8]. MicroRNAs have been impli- cardiac ATG7 expression, familial ATG5 mutations are asso- cated as mediators of circulating cardiovascular risk [10]. ciated with severe cardiac hypertrophy leading to dilated −/− Cardiac exosomes have also emerged as possible molecu- cardiomyopathy by 10 months [55]. In mice, ATG7 or −/− lar vehicles that facilitate crosstalk between the heart and ATG5 leads to cardiomyopathy characterized by inhibited end-organ tissues [16]. A recent study by Mentowski et al. autophagy and induced mesenchymal transition and apop- demonstrated that engineered exosomes can stimulate car- tosis [45, 46, 50, 57]. Conversely, in vivo overexpression diomyocyte hypertrophy [30]. Therefore, the indirect testing ATG7 in mice improves autophagic capacity that ameliorates 1 3 9 Page 12 of 16 Basic Research in Cardiology (2023) 118:9 Table 1 Differentially methylated genomic regions 1 3 Basic Research in Cardiology (2023) 118:9 Page 13 of 16 9 Fig. 6 ATG7 as an indirect candidate biomarker of CREB1 activ- ▸ ATG7: cg03800765 ity in plasma-treated iPSCs. A Scatterplot correlation between CpG R = − 0.61, p = 0.0026 B3 B6 methylation of iPSC-CMs treated with plasma from DCM (cyan) B1 B4 A4 control (grey) patients at cg03800765 and RNA-sequencing based A5 gene expression of ATG7 (normalized counts). Also illustrated is the A7 A6 negative linear trend (blue line, R = 0.61, P = 0.0026) with 95% con- A3 C1 C3 fidence region (gray). B Location of the CpG site cg03800765 in a B7 C6 C7 CpG island adjacent to the ATG7 gene, demonstrating overlap with A1 the CREB1 motif (MEME suite). C Putative downstream DMCs C9 overlapping CREB1 response element A2 C8 C2 −1 C10 C5 B5 C4 desmin-related cardiomyopathy [2]. Therefore, the differ - −2 ential methylation of ATG7 may represent a phenotypically pertinent observation. However, it remains to be shown −1 01 2 whether perturbation of the ATG7 promoter methylation CpG Methylation (normalized beta) indeed causes alterations in gene expression. 10 10 100 100 10000 Limitations Although the current study and analysis provide novel 80 80 80 80 80 insights into the diagnostic potential of indirect epig- enomic testing, some limitations must be considered. 60 60 60 60 60 First, DCM etiology and medication history in our cohort could not be standardized with control subjects owing to limited supply of clinical data and tissue, respectively 40 40 40 40 40 (see Suppl. Table 1). Although the current descriptive study uncovers an indirect epigenetic signature in iPSC- 20 20 20 20 20 CMs following treatment with plasma of HF patients, future studies should consider early, etiology-specific signatures of DNA methylation in larger cohorts to under- stand its diagnostic, and possibly predictive, potential in 02 02 02 02 0204 04 04 04 0406 06 06 06 0608 08 08 08 080001 01 01 100 100 00 00 00 human heart failure. Different etiologies of HF (e.g. HF 100% - Specificity 100% - Specificity% % with preserved ejection fraction) are possibly marked by a more systemic dysregulation of circulating metabolic factors, and thus might be even more suitable for indirect testing. Lastly, incorporation of other epigenetic marks, including histone modifications that are thought to be more signal responsive [27], may further improve the clinical precision of epigenetic testing. Conclusion In the current study, we provide the first evidence that cir - D culating factors drive indirect epigenomic alterations of iPSC-CMs and may therefore be useful for diagnostic test- ing. Diagnostic screening of cardiac biopsies is unfeasible, whereas development and standardization of indirect epig- enomic testing using blood plasma or serum may circumvent this limitation. 1 3 Gene Expression (Norm. Exp.) Sensitivity% Sensitivity% 9 Page 14 of 16 Basic Research in Cardiology (2023) 118:9 Supplementary Information The online version contains supplemen- 2. Bhuiyan MS, Pattison JS, Osinska H, James J, Gulick J, McLen- tary material available at https://doi. or g/10. 1007/ s00395- 022- 00954-3 . don PM, Hill JA, Sadoshima J, Robbins J (2013) Enhanced autophagy ameliorates cardiac proteinopathy. J Clin Invest Acknowledgements We thank Joshua Hartmann, Sabine Kuss, Jutta 123:5284–5297. https:// doi. org/ 10. 1172/ JCI70 877 Krebs and Ulrike Oehl for their technical support. 3. Bird AP (1986) CpG-rich islands and the function of DNA methylation. Nature 321:209–213. https:// doi. or g/ 10. 1038/ Author contributions C.U.O. designed and performed experiments, 32120 9a0 helped with the figures and wrote the manuscript with M.E.P.; M.E.P. 4. Burke MA, Cook SA, Seidman JG, Seidman CE (2016) Clinical performed bioinformatic analysis, created figures, and wrote the manu- and mechanistic insights into the genetics of cardiomyopathy. J script with C.U.O.; K.B.S. optimized the automated cell imaging assay, Am Coll Cardiol 68:2871–2886. https://do i.o rg/10 .10 16/j.ja cc. designed and performed experiments and analyzed data ; C.P., A.S.A., 2016. 08. 079 T.S.M., K.G., C.P., K.B.S. and D.W. performed experiments and pro- 5. Chen H, Orozco LD, Wang J, Rau CD, Rubbi L, Ren S, Wang Y, vided materials. T.W., F.S.H. and B.M. provided patient samples; D.S. Pellegrini M, Lusis AJ, Vondriska TM (2016) DNA methylation designed and performed experiments and provided critical oversight indicates susceptibility to isoproterenol-induced cardiac pathology regarding methods, interpretation and revision of the manuscript.; Y.A. and is associated with chromatin states. Circ Res 118:786–797. helped with the original analysis; J.B- designed the project and pro-https:// doi. org/ 10. 1161/ CIRCR ESAHA. 115. 305298 vided critical oversight regarding funding, methods, interpretation, and 6. Collier JJ, Suomi F, Olahova M, McWilliams TG, Taylor RW revision of the manuscript. J.B. and DS are the guarantors of this work (2021) Emerging roles of ATG7 in human health and disease. and accept responsibility for its integrity. All authors read, edited, and EMBO Mol Med 13:e14824. https:// doi. org/ 10. 15252/ emmm. approved the final manuscript.20211 4824 7. Decock A, Ongenaert M, Cannoodt R, Verniers K, De Wilde Funding Open Access funding enabled and organized by Projekt B, Laureys G, Van Roy N, Berbegall AP, Bienertova-Vasku J, DEAL. J.B. was supported by grants from the MWK (‘Development Bown N, Clement N, Combaret V, Haber M, Hoyoux C, Murray of an indirect cardiomyocyte test for the prediction of heart failure’; J, Noguera R, Pierron G, Schleiermacher G, Schulte JH, Stall- AZ 32–5400/58/2), and the DZHK (Deutsches Zentrum für Herz-Kre- ings RL, Tweddle DA, De Preter K, Speleman F, Vandesompele islauf-Forschung—German Centre for Cardiovascular Research) and J (2016) Methyl-CpG-binding domain sequencing reveals a the BMBF (German Ministry of Education and Research). M.E.P. was prognostic methylation signature in neuroblastoma. Oncotarget supported by the Alexander von Humboldt Forschungsstipendium, the 7:1960–1972. https:// doi. org/ 10. 18632/ oncot arget. 6477 Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), and the 8. Diakos NA, Taleb I, Kyriakopoulos CP, Shah KS, Javan H, Rich- Deutsche Gesellschaft für Kardiologie. C.U.O. was supported by DFG ins TJ, Yin MY, Yen CG, Dranow E, Bonios MJ, Alharethi R, (OE 688/1–1), BIH Charité Clinician Scientist Program, and Orlovic- Koliopoulou AG, Taleb M, Fang JC, Selzman CH, Stellos K, Dra- Nachwuchsfonds Innovative Cardiology. B.M. was supported by the kos SG (2021) Circulating and myocardial cytokines predict car- DZHK (Deutsches Zentrum für Herz-Kreislauf-Forschung—German diac structural and functional improvement in patients with heart Centre for Cardiovascular Research) and by the BMBF (German Min- failure undergoing mechanical circulatory support. J Am Heart istry of Education and Research). J.B., T.W. and B.M. were supported Assoc 10:e020238. https:// doi. org/ 10. 1161/ JAHA. 120. 020238 by the Collaborative Research Center 1550 (CRC1550 /SFB1550) 9. Fornaro L, Vivaldi C, Caparello C, Musettini G, Baldini E, Masi ‘Molecular Circuits of Heart Disease’ – Project-ID 464424253. G, Falcone A (2016) Pharmacoepigenetics in gastrointestinal tumors: MGMT methylation and beyond. Front Biosci (Elite Ed) 8:170–180 Declarations 10. Galluzzo A, Gallo S, Pardini B, Birolo G, Fariselli P, Boretto P, Vitacolonna A, Peraldo-Neia C, Spilinga M, Volpe A, Celentani Conflict of interest JB holds a patent on "In vitro method for cardio- D, Pidello S, Bonzano A, Matullo G, Giustetto C, Bergerone S, vascular risk stratification, EP2954322B1". Crepaldi T (2021) Identification of novel circulating microRNAs in advanced heart failure by next-generation sequencing. ESC Open Access This article is licensed under a Creative Commons Attri- Heart Fail 8:2907–2919. https:// doi. org/ 10. 1002/ ehf2. 13371 bution 4.0 International License, which permits use, sharing, adapta- 11. Gi WT, Haas J, Sedaghat-Hamedani F, Kayvanpour E, Tappu R, tion, distribution and reproduction in any medium or format, as long Lehmann DH, Shirvani Samani O, Wisdom M, Keller A, Katus as you give appropriate credit to the original author(s) and the source, HA, Meder B (2020) Epigenetic regulation of alternative mRNA provide a link to the Creative Commons licence, and indicate if changes splicing in dilated cardiomyopathy. J Clin Med. https://d oi.o rg/1 0. were made. The images or other third party material in this article are 3390/ jcm90 51499 included in the article's Creative Commons licence, unless indicated 12. Gillette TG, Hill JA (2015) Readers, writers, and erasers: chroma- otherwise in a credit line to the material. If material is not included in tin as the whiteboard of heart disease. Circ Res 116:1245–1253. the article's Creative Commons licence and your intended use is not https:// doi. org/ 10. 1161/ CIRCR ESAHA. 116. 303630 permitted by statutory regulation or exceeds the permitted use, you will 13. Haas J, Frese KS, Park YJ, Keller A, Vogel B, Lindroth AM, need to obtain permission directly from the copyright holder. To view a Weichenhan D, Franke J, Fischer S, Bauer A, Marquart S, Sed- copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . aghat-Hamedani F, Kayvanpour E, Kohler D, Wolf NM, Hassel S, Nietsch R, Wieland T, Ehlermann P, Schultz JH, Dosch A, Mereles D, Hardt S, Backs J, Hoheisel JD, Plass C, Katus HA, Meder B (2013) Alterations in cardiac DNA methylation in human References dilated cardiomyopathy. EMBO Mol Med 5:413–429. https://doi. org/ 10. 1002/ emmm. 20120 1553 1. Alpert MA, Lavie CJ, Agrawal H, Aggarwal KB, Kumar SA 14. Haider S, Cordeddu L, Robinson E, Movassagh M, Siggens L, (2014) Obesity and heart failure: epidemiology, pathophysiology, Vujic A, Choy MK, Goddard M, Lio P, Foo R (2012) The land- clinical manifestations, and management. Transl Res 164:345– scape of DNA repeat elements in human heart failure. Genome 356. https:// doi. org/ 10. 1016/j. trsl. 2014. 04. 010 Biol 13:R90. https:// doi. org/ 10. 1186/ gb- 2012- 13- 10- r90 1 3 Basic Research in Cardiology (2023) 118:9 Page 15 of 16 9 15. Ho TT, Warr MR, Adelman ER, Lansinger OM, Flach J, Verovs- cardiomyocytes: a dynamic clamp study with virtual IK1. Front kaya EV, Figueroa ME, Passegue E (2017) Autophagy maintains Physiol 6:7. https:// doi. org/ 10. 3389/ fphys. 2015. 00007 the metabolism and function of young and old stem cells. Nature 30. Mentkowski KI, Lang JK (2019) Exosomes engineered to 543:205–210. https:// doi. org/ 10. 1038/ natur e21388 express a cardiomyocyte binding peptide demonstrate improved 16. Jadli AS, Parasor A, Gomes KP, Shandilya R, Patel VB (2021) cardiac retention in vivo. Sci Rep 9:10041. https:// doi. org/ 10. Exosomes in cardiovascular diseases: pathological potential of 1038/ s41598- 019- 46407-1 nano-messenger. Front Cardiovasc Med 8:767488. https:// doi. 31. Movassagh M, Choy MK, Knowles DA, Cordeddu L, Haider S, org/ 10. 3389/ fcvm. 2021. 767488 Down T, Siggens L, Vujic A, Simeoni I, Penkett C, Goddard M, 17. Jjingo D, Conley AB, Yi SV, Lunyak VV, Jordan IK (2012) On Lio P, Bennett MR, Foo RS (2011) Distinct epigenomic features the presence and role of human gene-body DNA methylation. in end-stage failing human hearts. Circulation 124:2411–2422. Oncotarget 3:462–474. https://doi. or g/10. 18632/ onco tar get.497 https:// doi. org/ 10. 1161/ CIRCU LATIO NAHA. 111. 040071 18. Kenny HC, Abel ED (2019) Heart failure in type 2 diabetes 32. Nahapetyan H, Moulis M, Grousset E, Faccini J, Grazide MH, mellitus. Circ Res 124:121–141. https://doi. or g/10. 1161/ CIR CR Mucher E, Elbaz M, Martinet W, Vindis C (2019) Altered mito- ESAHA. 118. 311371 chondrial quality control in Atg7-deficient VSMCs promotes 19. Kronlage M, Dewenter M, Grosso J, Fleming T, Oehl U, enhanced apoptosis and is linked to unstable atherosclerotic Lehmann LH, Falcao-Pires I, Leite-Moreira AF, Volk N, Grone plaque phenotype. Cell Death Dis. https:// doi. or g/ 10. 1038/ HJ, Muller OJ, Sickmann A, Katus HA, Backs J (2019) O-Glc-s41419- 019- 1400-0 NAcylation of histone deacetylase 4 protects the diabetic heart 33. Oeing CU, Mishra S, Dunkerly-Eyring BL, Ranek MJ (2020) from failure. Circulation 140:580–594. https://doi .or g/10 .11 61/ Targeting protein kinase G to treat cardiac proteotoxicity. Front CIRCU LATIO NAHA. 117. 031942 Physiol 11:858. https:// doi. org/ 10. 3389/ fphys. 2020. 00858 20. Lehmann LH, Worst BC, Stanmore DA, Backs J (2014) His- 34. Oeing CU, Nakamura T, Pan S, Mishra S, Dunkerly-Eyring BL, tone deacetylase signaling in cardioprotection. Cell Mol Life Kokkonen-Simon KM, Lin BL, Chen A, Zhu G, Bedja D, Lee DI, Sci 71:1673–1690. https://d oi.o rg/1 0.1 007/s 00018-0 13-1 516-9 Kass DA, Ranek MJ (2020) PKG1alpha Cysteine-42 redox state 21. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, controls mTORC1 activation in pathological cardiac hypertrophy. Marth G, Abecasis G, Durbin R (2009) The sequence alignment/ Circ Res 127:522–533. https:// doi. org/ 10. 1161/ CIRCR ESAHA. map format and SAMtools. Bioinformatics 25:2078–2079119. 315714 22. Love MI, Huber W, Anders S (2014) Moderated estimation of 35. Pepin ME, Drakos S, Ha CM, Tristani-Firouzi M, Selzman CH, fold change and dispersion for RNA-seq data with DESeq2. Fang JC, Wende AR, Wever-Pinzon O (2019) DNA methylation Genome Biol 15:550 reprograms cardiac metabolic gene expression in end-stage human 23. Maksimovic J, Gordon L, Oshlack A (2012) SWAN: Subset- heart failure. Am J Physiol Heart Circ Physiol 317:H674–H684. quantile within array normalization for illumina infinium https:// doi. org/ 10. 1152/ ajphe art. 00016. 2019 HumanMethylation450 BeadChips. Genome Biol 13:R44. 36. Pepin ME, Ha CM, Crossman DK, Litovsky SH, Varambally S, https:// doi. org/ 10. 1186/ gb- 2012- 13-6- r44 Barchue JP, Pamboukian SV, Diakos NA, Drakos SG, Pogwizd 24. Marzese DM, Witz IP, Kelly DF, Hoon DS (2015) Epigenomic SM, Wende AR (2019) Genome-wide DNA methylation encodes landscape of melanoma progression to brain metastasis: unex- cardiac transcriptional reprogramming in human ischemic plored therapeutic alternatives. Epigenomics 7:1303–1311. heart failure. Lab Invest 99:371–386. https:// doi. org/ 10. 1038/ https:// doi. org/ 10. 2217/ epi. 15. 77s41374- 018- 0104-x 25. Mazzarotto F, Tayal U, Buchan RJ, Midwinter W, Wilk A, Whif- 37. Pepin ME, Ha CM, Potter LA, Bakshi S, Barchue JP, Haj Asaad A, fin N, Govind R, Mazaika E, de Marvao A, Dawes TJW, Felkin Pogwizd SM, Pamboukian SV, Hidalgo BA, Vickers SM, Wende LE, Ahmad M, Theotokis PI, Edwards E, Ing AY, Thomson KL, AR (2021) Racial and socioeconomic disparity associates with Chan LLH, Sim D, Baksi AJ, Pantazis A, Roberts AM, Watkins differences in cardiac DNA methylation among men with end- H, Funke B, O’Regan DP, Olivotto I, Barton PJR, Prasad SK, stage heart failure. Am J Physiol Heart Circ Physiol 320:H2066– Cook SA, Ware JS, Walsh R (2020) Reevaluating the genetic H2079. https:// doi. org/ 10. 1152/ ajphe art. 00036. 2021 contribution of monogenic dilated cardiomyopathy. Circulation 38. Pepin ME, Padgett LE, McDowell RE, Burg AR, Brahma MK, 141:387–398. https:// doi. or g/ 10. 1161/ CIR CU L A TIO N AHA . Holleman C, Kim T, Crossman D, Kutsch O, Hubert MT (2018) 119. 037661 Antiretroviral therapy potentiates high-fat diet induced obesity 26. McKinsey TA (2012) Therapeutic potential for HDAC inhibitors and glucose intolerance. Molecular metabolism 12:48–61 in the heart. Annu Rev Pharmacol Toxicol 52:303–319. https:// 39. Rodeheffer RJ (2011) Hypertension and heart failure: the ALL - doi. org/ 10. 1146/ annur ev- pharm tox- 010611- 134712 HAT imperative. Circulation 124:1803–1805. https:// doi. org/ 10. 27. McKinsey TA, Vondriska TM, Wang Y (2018) Epigenomic 1161/ CIRCU LATIO NAHA. 111. 059303 regulation of heart failure: integrating histone marks, long non- 40. Schafer M, Oeing CU, Rohm M, Baysal-Temel E, Lehmann LH, coding RNAs, and chromatin architecture. F1000Res. https:// Bauer R, Volz HC, Boutros M, Sohn D, Sticht C, Gretz N, Eichel- doi. org/ 10. 12688/ f1000 resea rch. 15797.1 baum K, Werner T, Hirt MN, Eschenhagen T, Muller-Decker K, 28. Meder B, Haas J, Sedaghat-Hamedani F, Kayvanpour E, Frese Strobel O, Hackert T, Krijgsveld J, Katus HA, Berriel Diaz M, K, Lai A, Nietsch R, Scheiner C, Mester S, Bordalo DM, Amr Backs J, Herzig S (2016) Ataxin-10 is part of a cachexokine cock- A, Dietrich C, Pils D, Siede D, Hund H, Bauer A, Holzer DB, tail triggering cardiac metabolic dysfunction in cancer cachexia. Ruhparwar A, Mueller-Hennessen M, Weichenhan D, Plass Mol Metab 5:67–78. https:// doi. org/ 10. 1016/j. molmet. 2015. 11. C, Weis T, Backs J, Wuerstle M, Keller A, Katus HA, Posch 004 AE (2017) Epigenome-wide association study identifies car - 41. Siede D, Rapti K, Gorska AA, Katus HA, Altmuller J, Boeckel JN, diac gene patterning and a novel class of biomarkers for heart Meder B, Maack C, Volkers M, Muller OJ, Backs J, Dieterich C failure. Circulation 136:1528–1544. https:// doi. org/ 10. 1161/ (2017) Identification of circular RNAs with host gene-independent CIRCU LATIO NAHA. 117. 027355 expression in human model systems for cardiac differentiation and 29. Meijer van Putten RM, Mengarelli I, Guan K, Zegers JG, disease. J Mol Cell Cardiol 109:48–56. https:// doi. org/ 10. 1016/j. van Ginneken AC, Verkerk AO, Wilders R (2015) Ion chan-yjmcc. 2017. 06. 015 nelopathies in human induced pluripotent stem cell derived 42. Smith NL, Felix JF, Morrison AC, Demissie S, Glazer NL, Loehr LR, Cupples LA, Dehghan A, Lumley T, Rosamond WD, Lieb 1 3 9 Page 16 of 16 Basic Research in Cardiology (2023) 118:9 W, Rivadeneira F, Bis JC, Folsom AR, Benjamin E, Aulchenko 50. Xu CN, Kong LH, Ding P, Liu Y, Fan ZG, Gao EH, Yang J, Yang YS, Haritunians T, Couper D, Murabito J, Wang YA, Stricker LF (2020) Melatonin ameliorates pressure overload-induced car- BH, Gottdiener JS, Chang PP, Wang TJ, Rice KM, Hofman A, diac hypertrophy by attenuating Atg5-dependent autophagy and Heckbert SR, Fox ER, O’Donnell CJ, Uitterlinden AG, Rotter activating the Akt/mTOR pathway. Biochim Biophys Acta Mol JI, Willerson JT, Levy D, van Duijn CM, Psaty BM, Witteman Basis Dis 1866:165848. https:// doi. org/ 10. 1016/j. bbadis. 2020. JC, Boerwinkle E, Vasan RS (2010) Association of genome- 165848 wide variation with the risk of incident heart failure in adults of 51. Yang J, Xu WW, Hu SJ (2015) Heart failure: advanced develop- European and African ancestry: a prospective meta-analysis from ment in genetics and epigenetics. Biomed Res Int 2015:352734. the cohorts for heart and aging research in genomic epidemiol-https:// doi. org/ 10. 1155/ 2015/ 352734 ogy (CHARGE) consortium. Circ Cardiovasc Genet 3:256–266. 52. Yen CY, Huang HW, Shu CW, Hou MF, Yuan SS, Wang HR, https:// doi. org/ 10. 1161/ CIRCG ENETI CS. 109. 895763 Chang YT, Farooqi AA, Tang JY, Chang HW (2016) DNA 43. Smyth GK (2004) Linear models and empirical bayes methods for methylation, histone acetylation and methylation of epigenetic assessing differential expression in microarray experiments. Stat modifications as a therapeutic approach for cancers. Cancer Lett Appl Genet Mol Biol 3:Article3. https:// doi. org/ 10. 2202/ 1544- 373:185–192. https:// doi. org/ 10. 1016/j. canlet. 2016. 01. 036 6115. 1027 53. Zannas AS, Jia M, Hafner K, Baumert J, Wiechmann T, Pape JC, 44. Stratton MS, McKinsey TA (2016) Epigenetic regulation of car- Arloth J, Kodel M, Martinelli S, Roitman M, Roh S, Haehle A, diac fibrosis. J Mol Cell Cardiol 92:206–213. https:// doi. org/ 10. Emeny RT, Iurato S, Carrillo-Roa T, Lahti J, Raikkonen K, Eriks- 1016/j. yjmcc. 2016. 02. 011 son JG, Drake AJ, Waldenberger M, Wahl S, Kunze S, Lucae S, 45. Takagaki Y, Lee SM, Dongqing Z, Kitada M, Kanasaki K, Koya D Bradley B, Gieger C, Hausch F, Smith AK, Ressler KJ, Muller- (2020) Endothelial autophagy deficiency induces IL6 - dependent Myhsok B, Ladwig KH, Rein T, Gassen NC, Binder EB (2019) endothelial mesenchymal transition and organ fibrosis. Autophagy Epigenetic upregulation of FKBP5 by aging and stress contributes 16:1905–1914. https:// doi. org/ 10. 1080/ 15548 627. 2020. 17136 41 to NF-kappaB-driven inflammation and cardiovascular risk. Proc 46. Taneike M, Yamaguchi O, Nakai A, Hikoso S, Takeda T, Mizote Natl Acad Sci USA 116:11370–11379. https:// doi. org/ 10. 1073/ I, Oka T, Tamai T, Oyabu J, Murakawa T, Nishida K, Shimizu T, pnas. 18168 47116 Hori M, Komuro I, Takuji Shirasawa TS, Mizushima N, Otsu K 54. Zaphiriou A, Robb S, Murray-Thomas T, Mendez G, Fox K, (2010) Inhibition of autophagy in the heart induces age-related McDonagh T, Hardman SM, Dargie HJ, Cowie MR (2005) The cardiomyopathy. Autophagy 6:600–606. https:// doi. org/ 10. 4161/ diagnostic accuracy of plasma BNP and NTproBNP in patients auto.6. 5. 11947 referred from primary care with suspected heart failure: results 47. Tiedemann RL, Hlady RA, Hanavan PD, Lake DF, Tibes R, Lee of the UK natriuretic peptide study. Eur J Heart Fail 7:537–541. JH, Choi JH, Ho TH, Robertson KD (2016) Dynamic reprogram-https:// doi. org/ 10. 1016/j. ejhea rt. 2005. 01. 022 ming of DNA methylation in SETD2-deregulated renal cell car- 55. Zech ATL, Singh SR, Schlossarek S, Carrier L (2020) Autophagy cinoma. Oncotarget 7:1927–1946. https://doi. or g/10. 18632/ onco t in cardiomyopathies. Bba-Mol Cell Res. https://d oi.o rg/1 0.1 016/j. arget. 6481bbamcr. 2019. 01. 013 48. Tohyama S, Hattori F, Sano M, Hishiki T, Nagahata Y, Matsuura 56. Zhang YA, Ma X, Sathe A, Fujimoto J, Wistuba I, Lam S, Yatabe T, Hashimoto H, Suzuki T, Yamashita H, Satoh Y, Egashira T, Y, Wang YW, Stastny V, Gao B, Larsen JE, Girard L, Liu X, Song Seki T, Muraoka N, Yamakawa H, Ohgino Y, Tanaka T, Yoichi K, Behrens C, Kalhor N, Xie Y, Zhang MQ, Minna JD, Gazdar AF M, Yuasa S, Murata M, Suematsu M, Fukuda K (2013) Distinct (2016) Validation of SCT methylation as a Hallmark biomarker metabolic flow enables large-scale purification of mouse and for lung cancers. J Thorac Oncol 11:346–360. https:// doi. org/ 10. human pluripotent stem cell-derived cardiomyocytes. Cell Stem 1016/j. jtho. 2015. 11. 004 Cell 12:127–137. https:// doi. org/ 10. 1016/j. stem. 2012. 09. 013 57. Zhang Z, Zhang S, Wang Y, Yang M, Zhang N, Jin Z, Ding L, 49. Voelter-Mahlknecht S (2016) Epigenetic associations in relation Jiang W, Yang J, Sun Z, Qiu C, Hu T (2017) Autophagy inhib- to cardiovascular prevention and therapeutics. Clin Epigenetics its high glucose induced cardiac microvascular endothelial cells 8:4. https:// doi. org/ 10. 1186/ s13148- 016- 0170-0 apoptosis by mTOR signal pathway. Apoptosis 22:1510–1523. https:// doi. org/ 10. 1007/ s10495- 017- 1398-7 1 3
Journal
Basic Research in Cardiology – Springer Journals
Published: Mar 20, 2023
Keywords: Precision medicine; Epigenetics; Heart failure; DNA methylation; Pilot study