DNMT1 protein, human

Dataset 1: 450K dataset of a total of 39 methylation laboratory standard control samples reported by [13 (link)]. Human unmethylated DNA (HCT116 double knock out (DKO) of both DNA methyltransferases DNMT1 (-/-) and DNMT3b (-/-)) and fully methylated DNA (HCT116 DKO DNA enzymatically methylated) were obtained commercially (Zymo Research, Irving CA) and mixed together in different proportions to create laboratory control samples with specific methylation levels: 0, 5, 10, 20, 40, 50, 60, 80 and 100% methylated. Replicates for each methylation level (n = 10, 3, 2, 3, 3, 2, 3, 3 and 10, respectively) were independently assayed on different arrays.
Dataset 2: 450K dataset of 22 samples reported by [4 (link)]. These samples included three replicates from the HCT116 WT cell-line, three replicates from the HCT116 DNMT1 and DNMT3B double KO (DKO) cell-line, and 16 other samples (GEO accession number: GSE29290). In particular to evaluate RELIC and other dye-bias correction methods, we used the six replicates from the HCT116 WT and HCT116 DKO cell-lines, and the matched bisulfite pyrosequencing (BPS) data for 15 probes in the two cell-lines reported in the Table one of [4 (link)]. As described in [4 (link)] the fifteen CpGs were selected for technical validation of the 450K array measures (six sites for Infinium I assay and nine sites for Infinium II assay) using the more accurate BPS method as the “gold standard”.
Dataset 3: 450K dataset of 24 samples reported by [6 (link)]. These samples included 12 blood samples and 12 saliva samples for ten individuals, with two individuals having two technical blood/saliva replicates (GEO accession number: GSE73745). More specifically, we used these samples and the matched bisulfite pyrosequencing (BPS) data for three probes (cg19754622, cg16106427, cg08899523) to evaluate RELIC and other dye-bias correction methods.

Values are shown as the mean ± standard error of mean (SEM), and error bars for scatter dot plots represent one SEM. Since aerobic capacity and cardiac fibrosis are significant clinical outcomes related to the survival of HF patients [4 (link), 8 (link)], power (1- β) analysis for paired sample t tests used to compare the difference in $V$ O_2peak and ECV fractions before and after HIIT. Differences in physical PCS, MCS, and LVWMS were estimated by the chi-square test.
The nonparametric test was used in the study owing to the limited sample size. The Wilcoxon signed rank test was conducted to estimate within-group differences between data before and after HIIT, including exercise capacity function, CMR-LGE results (LV geometry, functions, and ECV fractions), and blood chemistry data. The Mann‒Whitney U test was used to estimate differences in selected protein amounts obtained from LC‒MS results and methylation levels between cells incubated in patient serum before and after HIIT. Relationships between the DNMT1 levels and health-related physical fitness and CMR-LGE findings were assessed by Spearman’s correlation analysis.
Relative protein expression (measurements/baseline) of VLCAD, Cyto C, CASP3, lamin B1, actin and Arp2 in HCFs between the original and knockdown of ACADVL was compared by the Mann‒Whitney U test. This test was also used to assess mitochondrial intensity in HCFs treated with patient serum before and after HIIT and in cells with and without ACADVL knockdown. Kruskall-Wallis test was conducted to assess cell migration speed in three different culture media and with different cell numbers at different times (baseline, 24 h and 48 h after inoculation). Multiple comparisons Dunn’s test was used to estimate differences of cell behaviours between each of the above sampling time. The relationships between normalized changes (  $\mathrm{\Delta }\text{Value}={\displaystyle \frac{{\text{Value}}_{\text{post}-\text{HIIT}}-{\text{Value}}_{\text{pre}-\text{HIIT}}}{{\text{Value}}_{\text{pre}-\text{HIIT}}}}$ ) in exercise performance and CMR-LGE measurements after HIIT were estimated by Spearman correlation and partial correlation analysis after controlling LV mass. All statistical assessments were considered significant at p < 0.05.

The ACADVL gene encodes for very long-chain acyl-CoA dehydrogenase (VLCAD), which functions within mitochondria and is essential for fatty acid oxidation. HCFs were prepared for western blot analysis of VLCAD, caspase-3 (CASP3), cytochrome c (Cyto C), lamin B1, β-actin, and Arp2 with the internal reference protein glyceraldehyde 3-phosphate dehydrogenase (GAPDH) before knockdown of the ACADVL gene. The above proteins were quantified again after knockdown of ACADVL. Detailed methods of the western blotting and knockdown procedure are provided in Additional file 2.
Knockdown of DNMT1 leads to generally decreased DNA methylation and activates cascades of genotoxic stress [31 (link)] in cells, resulting in signal transduction unrelated to cardiac fibrosis. Thus, we preferred to downregulate the ACADVL gene expression to simulate the HIIT-associated inhibition of human cardiac fibroblast activities.

To test for an association between overall piRNA or KRAB-ZFP pathway activity and genome size, we first compiled male and female gonad RNA-Seq datasets for vertebrates of diverse genome sizes, including P. ornatum (ornate burrowing frog), Gallus gallus (chicken), D. rerio (zebrafish), Xenopus tropicalis (Western clawed frog), A. carolinensis (green anole), Mus musculus (mouse), Geotrypetes seraphini (Gaboon caecilian), Rhinatrema bivittatum (two-lined caecilian), and Caecilia tentaculata (bearded caecilian) spanning genomes sizes from 1.0—5.5 Gb, and P. waltl (the Iberian ribbed newt), A. mexicanum (the Mexican axolotl), C. orientalis (the fire-bellied newt), P. annectens, and P. aethiopicus (African and marbled lungfishes) spanning genome sizes from 20—∼130 Gb (Supplementary Files S8,S9). We performed de novo assemblies using the same pipeline as for R. sibiricus on all obtained datasets.
We identified transcripts of 21 genes receiving a direct annotation of piRNA processing in vertebrates in the Gene Ontology knowledgebase that were present in the majority of our target species: ASZ1, BTBD18 (BTBDI), DDX4, EXD1, FKBP6, GPAT2, HENMT1 (HENMT), MAEL, MOV10l1 (M10L1), PIWIL1, PIWIL2, PIWIL4, PLD6, TDRD1, TDRD5, TDRD6, TDRD7, TDRD9, TDRD12 (TDR12), TDRD15 (TDR15), and TDRKH. In addition, we identified transcripts of 14 genes encoding proteins that create a transcriptionally repressive chromatin environment in response to recruitment by PIWI proteins or KRAB-ZFP proteins, 12 of which received a direct annotation of NuRD complex in the Gene Ontology knowledgebase and 2 of which were taken from the literature: CBX5, CHD3, CHD4, CSNK2A1 (CSK21), DNMT1, GATAD2A (P66A), MBD3, MTA1, MTA2, RBBP4, RBBP7, SALL1, SETDB1 (SETB1), and ZBTB7A (ZBT7A) (Ecco et al., 2017 (link); Wang et al., 2023 (link)). Finally, we identified TRIM28, which bridges this repressive complex to TE-bound KRAB-ZFP proteins in tetrapods, lungfishes, and coelacanths (Ecco et al., 2017 (link)). For comparison, we identified transcripts of 14 protein-coding genes receiving a direct annotation of miRNA processing in vertebrates in the Gene Ontology knowledgebase, which we did not predict to differ in expression based on genome size: ADAR (DSRAD), AGO1, AGO2, AGO3, AGO4, DICER1, NUP155 (NU155), PUM1, PUM2, SNIP1, SPOUT1 (CI114), TARBP2 (TRBP2), TRIM71 (LIN41), and ZC3H7B. Expression levels for each transcript in each individual were measured with Salmon (Patro et al., 2017 (link)) (Supplementary File S10).
As a proxy for overall piRNA silencing activity, for each individual, we calculated the ratio of total piRNA pathway expression (summed TPM of 21 genes) to total miRNA pathway expression (summed TPM of 14 genes). As a proxy for transcriptional repression driven by both the piRNA pathway and KRAB-ZFP binding activity, we calculated the ratio of total transcriptional repression machinery expression (summed TPM of 14 genes) to total miRNA pathway expression. Finally, we calculated the ratio of TRIM28 expression to total miRNA pathway expression for each individual. We also calculated these ratios with a more conservative dataset allowing for no missing genes; this yielded 15 piRNA pathway genes, 9 KRAB-ZFP genes, and 13 miRNA genes. We plotted these ratios to reveal any relationship between TE silencing pathway expression and genome size.