Integrative multi-omic profiling of the chronically hypoxic heart: focus on m6A and m6Am epitranscriptomic regulation

Reduced oxygen availability is an environmental factor characteristic of high-altitude conditions that plays a critical role in shaping cellular homeostasis and epigenomic regulation. Adaptation to various models of chronic hypoxia represents a well-recognized physiological process that enhances cardiac tolerance to ischemic stress; however, the molecular mechanisms coordinating metabolic, proteomic, and post-transcriptional remodeling in this adaptive response to low-oxygen conditions remain insufficiently understood. Here, we combined quantitative metabolomic, lipidomic, and proteomic profiling with targeted protein analyses to characterize the molecular landscape of rat hearts adapted to continuous normobaric hypoxia (CNH, 10% O₂ for 3 weeks). Multi-omics integration revealed tightly coupled remodeling across metabolic and structural domains, consistent with enhanced energetic efficiency and oxidative stress resistance. Pathway enrichment identified coordinated activation of energy reprogramming (AMPK, glycolysis, and PPAR signaling), reinforcement of antioxidant defense (glutathione metabolism), membrane remodeling (glycerophospholipid and peroxisomal pathways), and protein quality control (autophagy–lysosome and proteasome systems). Beyond these canonical adaptive responses, CNH markedly affected the epitranscriptomic machinery: both m⁶A demethylases ALKBH5 and FTO – enzymes previously linked to cardioprotective effects – were upregulated, accompanied by increased abundance of multiple m⁶A readers (YTHDF1–3, YTHDC1), whereas methyltransferases METTL3 and PCIF1 remained stable. At the level of RNA modifications, global m⁶A levels in total RNA were unchanged, whereas m⁶Am levels were significantly increased under hypoxia. These results demonstrate that chronic hypoxia reprograms the heart not only at the metabolic and proteomic levels but also through epitranscriptomic regulation, suggesting that RNA methylation dynamics may contribute to the cardioprotective phenotype. Collectively, our findings provide a system-level framework linking metabolic flexibility, redox balance, and post-transcriptional control during hypoxic adaptation.

1 Introduction

Adaptation to chronic hypoxia represents a quintessential example of how environmental factors can reprogram cellular and molecular networks to enhance physiological resistance to acute hypoxia. In the heart, sustained exposure to low oxygen levels elicits a well-established adaptive response that increases myocardial tolerance to acute ischemic injury (Kolar and Ostadal, 2004). This phenomenon, first recognized in the 1960s in populations living at high altitudes under chronically low oxygen conditions, has long intrigued both basic scientists and clinicians because of its potential to mitigate the burden of ischemic heart disease (IHD) (Hurtado, 1960). Despite decades of research, however, the direct clinical application of hypoxia-based interventions remains limited. The major barrier lies in the unpredictable nature of acute ischemic events, which often preclude preconditioning strategies based on controlled hypoxic exposure. Nevertheless, elucidating the cellular and molecular mechanisms underlying hypoxia-induced cardioprotection remains of paramount importance. By identifying the signaling networks and molecular effectors that mediate this endogenous defense, we may uncover pharmacologically targetable pathways that could be exploited prophylactically – or even acutely – offering more practical therapeutic strategies than hypoxic preconditioning itself.

At the molecular level, chronic hypoxia activates a coordinated transcriptional response primarily mediated by hypoxia-inducible factors (HIFs), which regulate processes such as erythropoiesis, angiogenesis, and metabolic reprogramming from oxidative phosphorylation to glycolysis, through transcriptional control of more than 1,000 target genes (Seagroves et al., 2001; Benak et al., 2025). Beyond HIFs, several other molecular mediators – including ATP-sensitive potassium channels, reactive oxygen species, nitric oxide, and a variety of kinases (e.g., protein kinase C, mitogen-activated protein kinases) – have been implicated in hypoxia-induced cardioprotection (Kolar and Ostadal, 2004; Chytilova et al., 2015; Hlavackova et al., 2010; Hlavackova et al., 2007; Micova et al., 2016; Alanova et al., 2017). However, despite these advances, our current understanding of this complex adaptive response remains fragmented.

Recent advances in multi-omics technologies have made it possible to interrogate this complexity directly. Integrating proteomic, metabolomic, and lipidomic data enables the discovery of coordinated networks underlying energetic reprogramming, membrane remodeling, and stress adaptation in the heart. However, some key regulatory processes – particularly those involving epitranscriptomic modifications – remain difficult to capture by standard proteomic workflows. These modifications, such as N⁶-methyladenosine (m⁶A) and N⁶,2′-O-dimethyladenosine (m⁶Am), modulate RNA stability, splicing, and translation, and are controlled by distinct sets of “writers”, “erasers”, and “readers” (Kumar and Mohapatra, 2021; Benak et al., 2024a; Hlavackova et al., 2025; Benak et al., 2026; Benak et al., 2023; Oerum et al., 2021). Accumulating evidence indicates that hypoxia influences both the deposition and removal of these RNA marks, while HIF-1α itself is regulated by m⁶A-dependent mechanisms (Benak et al., 2025). Yet, the contribution of m⁶A/m⁶Am machinery to cardiac hypoxic adaptation remains poorly defined, partly because the low abundance of these regulators often limits their detection in discovery-based proteomics.

To address these knowledge gaps, the present study integrates proteomic, metabolomic, and lipidomic profiling of chronically hypoxic hearts to construct a system-level view of cardiac adaptation. Recognizing the underrepresentation of epitranscriptomic regulators in global proteomic datasets, we complemented this analysis with targeted Western blot quantification of major m⁶A/m⁶Am writers, erasers, and readers. Together, these complementary approaches aim to delineate the molecular landscape of chronic hypoxia–induced cardioprotection and identify candidate regulatory mechanisms that bridge metabolic adaptation and post-transcriptional control.

2 Materials and methods

Unless stated otherwise, all chemicals were obtained from Sigma-Aldrich (St. Louis, United States).

2.1 Animals and adaptation to chronic hypoxia

Adult (3–4 months old) male Sprague–Dawley rats (Center of Experimental Medicine of the Institute for Clinical and Experimental Medicine, Prague) were assigned to either continuous normobaric hypoxia (CNH; n = 4) or normoxic control conditions (n = 6). CNH animals were exposed to 10% O₂ in a normobaric chamber equipped with hypoxic generators (Everest Summit, Hypoxico, NY, United States) for 3 weeks, representing an established model of cardioprotective adaptation (Neckar et al., 2017). This model reproduces sustained systemic hypoxemia characteristic of high-altitude conditions in humans while avoiding confounding effects associated with changes in barometric pressure. No reoxygenation occurred during this period. Control rats were maintained at room air for the same duration. All animals were housed under controlled environmental conditions (23 °C; 12 h:12 h light–dark cycle; lights on at 6:00 a.m.) with free access to water and standard chow.

The animals for analysis were deeply anesthetized with pentobarbital (50 mg/kg) and killed by cervical dislocation immediately after the cessation of hypoxic exposure. Intact hypoxic and normoxic hearts were rapidly excised and washed in a cold (0 °C) saline. Hearts were dissected into the right ventricle (RV), the free wall of the left ventricle (LV), and the septum (Balkova et al., 2009). All collected tissue segments were weighed, frozen, and stored in liquid nitrogen until use. Experiments were performed in accordance with a valid experimental project, Permit No. 66/2021. The project is approved by the Animal Care and Use Committee of the Institute of Physiology of the Czech Academy of Sciences, as well as by the Resort Professional Commission of the CAS for Approval of Projects of Experiments on Animals. The animals were housed in facilities accredited by the Czech Ministry of Agriculture. Experiments were carried out under veterinary supervision, complying with Act No. 246/1992 Coll. and Decree No. 419/2012 Coll., implementing Directive 2010/63/EU of the European Parliament and of the Council regarding the protection of animals used for scientific purposes. The 3Rs principles were applied to the maximum extent possible.

2.2 Metabolomic and lipidomic analysis

Metabolomic and lipidomic analysis was performed to investigate the effects of CNH on LV tissue samples. Tissue samples were prepared following established protocols (Benak et al., 2024b; Sistilli et al., 2021; Tsugawa et al., 2020). For extraction, a biphasic solvent system of cold methanol, methyl tert-butyl ether (MTBE), and water (Cajka et al., 2017; Cajka et al., 2023) was used. Four different LC-MS platforms were used for metabolomics and lipidomics profiling: (i) metabolomics of polar metabolites in positive ion mode (BEH Amide platform), (ii) metabolomics of polar metabolites in negative ion mode (HSS T3 platform), (iii) lipidomics in positive ion mode (BEH C18 platform), (iv) lipidomics in negative ion mode (BEH C18 platform). List of annotated complex lipids and polar metabolites is in Supplementary Table S1.

Internal standards were added to the extraction and resuspension solvents and monitored throughout the analytical workflow to assess extraction efficiency and instrument stability. Sample injection order was randomized. Pooled quality control (QC) samples, prepared from aliquots of all LV extracts, were injected periodically throughout each LC–MS platform sequence. Procedural blanks were analyzed to monitor background contamination. Instrument performance and signal stability were evaluated based on internal standard responses, retention time consistency, and signal reproducibility across QC injections.

2.3 Proteomic analysis

Proteomic analysis was performed to investigate the effects of CNH on LV tissue. The samples were homogenized and peptides prepared according to established protocols (Benak et al., 2024c). In short, peptide mixtures were analyzed by liquid chromatography coupled with high-resolution mass spectrometry (Orbitrap Exploris 480 with FAIMS interface) using a data-independent acquisition (DIA) strategy. Peptides were separated on a C18 column with a 120-min gradient, and DIA settings included an MS1 scan range of m/z 350–1,500 at 60,000 resolutions, followed by staggered MS2 windows with high-energy collision dissociation. Acquired DIA datasets were processed in Spectronaut software, and proteins were quantified based on relative peptide abundance. Comparisons were made between CNH-adapted and normoxic LV samples.

Western blot analysis was employed to validate key findings from the proteomic study as described previously (Benak et al., 2024c). Significant upregulation of carbonic anhydrase 2 (CA2) and 15-lipoxygenase 1 (ALOX15) was confirmed in LV samples (Supplementary Figure S1), corroborating the proteomic data. These proteins were selected as representative targets based on their consistent regulation in the discovery dataset, established relevance to hypoxia-related metabolic and redox pathways, and the availability of well-validated antibodies suitable for quantitative immunoblotting.

2.4 Multi-omics data integration

To integrate proteomic, metabolomic, and lipidomic datasets and identify discriminating molecular features between experimental groups, we applied the DIABLO framework (Data Integration Analysis for Biomarker discovery using Latent cOmponents (Singh et al., 2019)) implemented in the mixOmics R package (Rohart et al., 2017). Features containing missing values were removed prior to analysis. Because the proteomic dataset was the largest (4,530 quantified proteins), the top 500 most variable proteins were retained to ensure balanced contribution across datasets. All detected metabolites (n = 102) and lipids (n = 479) were included. A multi-block partial least squares discriminant analysis (PLS-DA) was performed with parameters optimized for the present data. From each dataset, the 20 features with the highest loadings on each component were selected for downstream interpretation. Loadings were extracted for the first two components (Supplementary Figure S2; Supplementary Table S2). To explore cross-omics relationships among top discriminating variables, sparse generalised canonical correlation discriminant analysis (sgccda) was used (Figure 3D; Supplementary Table S3).

2.5 Tissue processing and western blotting analysis

As described earlier, tissue homogenization, protein separation (Semenovykh et al., 2022), and immunodetection (Holzerova et al., 2015) were performed. Frozen LV myocardium was pulverized in liquid nitrogen to a fine powder followed by Potter-Elvehjem homogenization in eight volumes of homogenization buffer [12.5 mM TRIS, 2.5 mM EGTA, 1 mM EDTA, 250 mM sucrose, 6 mM 2-mercaptoethanol, protease inhibitor cocktail (Roche Diagnostics, Germany) and phosphatase inhibitor cocktail (Roche Diagnostics, Germany), pH 7.4]. The protein concentration in homogenates was measured by the Bradford method (Bio-Rad, United States). The LV homogenates were subjected to SDS electrophoresis on 10% polyacrylamide gels (Mini-PROTEAN TetraCell; Bio-Rad, United States) and electrotransferred onto PVDF membranes (0.2 μm pore size; Bio-Rad, United States). Subsequently, membranes were blocked with 5% blotting-grade blocker (Bio-Rad, United States) in PBS containing Tween 20 (1%) for 1 h and incubated with appropriate primary and secondary antibodies (diluted in 1% blotting-grade blocker and 1% Tween 20 in PBS): anti-ALKBH5 (Abcam, United Kingdom; ab195377, 1:1,400, overnight), anti-FTO (Abcam, United Kingdom; ab92821, 1:1,000, overnight), anti-METTL3 (Abcam, United Kingdom; ab195352, 1:1,000, overnight), anti-METTL4 (Invitrogen, United States; PA5-97202, 1:1,400, overnight), anti-PCIF1 (Invitrogen, United States; PA5-110081, 1:1,400, overnight), anti-YTHDC1 (Abcam, United Kingdom; ab220159, 1:1,400, overnight), anti-YTHDC2 (Abcam, United Kingdom; ab220160, 1:1,400, overnight), anti-YTHDF1 (Abcam, United Kingdom; ab157542, 1:1,400, overnight), anti-YTHDF2 (Invitrogen, United States; PA5-70853, 1:1,400, overnight), anti-YTHDF3 (SAB21022736, 1:1,400, overnight), anti-mouse secondary antibody (Invitrogen, United States; 31432, 1:10,000, 1 h) and anti-rabbit secondary antibody (Bio-Rad, United States; 170–6515, 1:10,000, 1 h). The same amount of protein was loaded on the gels and all samples from all experimental groups were always analyzed on the same membrane. The same reference sample was run on each gel and was used for normalization. At the same time, results were recalculated to the total protein amount gained by Ponceau S staining (Semenovykh et al., 2022), a method preferred over the use of housekeeping proteins as loading controls (Moritz, 2017). Each biological sample was analyzed in at least three technical replicates, which were averaged to yield a single value per animal, and statistical analyses were performed using biological replicates. The membranes were visualized by enhanced chemiluminescence (ECL) substrates (SuperSignal™ West Dura Extended Duration Substrate or SuperSignal™ West Femto Maximum Sensitivity Substrate, Thermo Scientific, United States) using a ChemiDoc™ system (Bio-Rad, United States).

2.6 m⁶A/m⁶Am quantification in left ventricle tissue

According to the manufacturer’s instructions, total RNA was isolated from control and CNH adapted LVs with RNAzol® RT.

RNA samples (500–1,000 ng) were digested by 1 U of Nuclease P1 (M0660S, New England BioLabs, United States) in 50 mM ammonium acetate buffer (pH 5.5) and 0.3 U of snake venom phosphodiesterase (P3243, Merck, Germany) at 37 °C for 2 h. After that, the pH of the solution was adjusted to ≈8 (by addition of 1.5 ekv. of ammonium acetate pH 9.2) and all nucleotides were dephosphorylated by 0.01 U of Shrimp Alkaline Phosphatase (M0371S, New England BioLabs, United States) at 37 °C for 1 h. At this point reaction was spiked with the mixture of isotopically labeled D3-m⁶A (TRC-M275897, LGC, United States) and D3-m⁶Am (TRC-D447417, LGC, United States) standards (10 mM final concentration). Digested samples were filtered with Vivacon 500, 10 kDa centrifugal filters (VN01H02, Sartorius, Germany). The flow-through was transferred to the HPLC vial for subsequent LC-MS analysis.

The separation of the digested RNA samples was performed by HPLC system (Acquity Premier, Waters, United States) on a C18 column (Acquity Premier HSS T3, 1.8 µm, 2.1 × 100 mm, Waters, United States) at 35 °C using a gradient of water (A) and acetonitrile (B), each containing 0.1% (v/v) formic acid. The gradient was 0–2 min, 100% A; 8 min, 85% A; 8.5 min, 10% A; 11 min, 10% A; 11.1 min, 100% A; 17 min, 100% A. The flow rate was 0.25 mL/min. The autosampler was running at 10 °C and injection volume was 5 µL. The HPLC system was coupled online to a triple quadrupole mass spectrometer (Xevo TQ Absolute, Waters, United States). The source settings were: capillary voltage 3 kV, source temperature 150 °C, cone voltage 20 V, desolvation temperature 600 °C, desolvation gas flow, 600 L/h, positive ion mode. The instrument was operated in MRM (multiple reaction monitoring) mode with 0.024 s dwell time and following transitions: 282.075 > 150.03 (m⁶A), 285.138 > 153.029 (D3-m⁶A), 296.075 > 149.977 (m⁶Am), 299.138 > 153.010 (D3-m⁶Am).

Data analysis was conducted using TargetLynx XS V4.2 software. Quantification was achieved by measuring the MRM signal peak area (area under the curve, AUC) and applying single-point calibration based on the known value of a spiked isotopically labeled standards (Supplementary Figure S3).

2.7 Data processing and statistical analyses

For proteomic, metabolomic, and lipidomic analyses, all available biological replicates were used, comprising normoxic controls (n = 6) and CNH-adapted animals (n = 4). For immunochemical analyses, a subset of animals was used (n = 4 per group), as specified in the corresponding figure legends.

For metabolomic and lipidomic datasets, data were normalized using mTIC (total ion chromatogram of all annotated metabolites), log₁₀-transformed, and Pareto-scaled prior to statistical evaluation. Group comparisons were performed with unpaired two-tailed t-tests in the R environment (version 4.3.2). Metabolites were evaluated based on fold-change and p-value, and ranked according to variable importance in projection (VIP) scores from PLS-DA.

For proteomic datasets, raw files were processed in Spectronaut 14. Differential expression analysis was conducted in R (version 4.5.0) using the MSstats package (version 4.116.1) (Choi et al., 2014; Kohler et al., 2023). Data were normalized using a median-based approach and summarized by Tukey’s polish method. Proteins represented by only a single peptide were excluded. Missing values were imputed using the MSstats algorithm, except where entire proteins were absent across runs (Kohler et al., 2023). Proteins were considered differentially expressed at |log₂ fold change| ≥ 0.5 and p ≤ 0.05. Functional annotation of significant proteins was performed by over-representation analysis (ORA) against the KEGG database (Kanehisa, 2019; Kanehisa et al., 2025; Kanehisa and Goto, 2000) (https://www.genome.jp/).

For immunochemistry experiments, statistical analyses were performed in GraphPad Prism 8 (GraphPad Software, United States). Data are presented as mean ± SD or, where indicated, as median with interquartile range (box) and minimum–maximum values (whiskers). Between-group comparisons were performed using unpaired two-tailed Student’s t-tests. Differences were considered significant at p ≤ 0.05. The number of biological replicates (n) used for each analysis is indicated in the corresponding figure and table legends.

3 Results

3.1 Multi-omic analysis of the chronically hypoxic heart

To get a better understanding of the effect of CNH on the heart, proteomic, metabolomic, and lipidomic analysis were performed.

Metabolomic and lipidomic analyses revealed significant differences in cardiac metabolite and lipid profiles between the experimental groups. PCA demonstrated clear separation between groups, indicating substantial metabolic and lipidomic reprogramming under CNH (34% and 58% of explained variance, respectively; Figures 1A,B). DE analysis identified significant changes in 37 metabolites (Figure 1C) and 250 lipids (Figure 1D). These changes involved metabolites and lipids related to energy metabolism (e.g., glucose-6-phosphate, succinic acid) and membrane remodeling (e.g., phosphatidylglycerols and fatty acids), underscoring the metabolic and structural hypoxic adaptations. These results are summarized in Supplementary Table S4.

Figure 1

Figure 1. Metabolomics and lipidomics analyses in left ventricular samples between continuous normobaric hypoxia (CNH) and normoxic (N) groups. Principal component analysis (PCA) of the detected metabolites (A) and lipids (B) indicated high discrimination between the experimental groups. (C) Top DE metabolites selected by log2FC (Supplementary Table S1). (D) Top DE lipids selected by log2FC (Supplementary Table S1).

Proteomic analysis revealed distinct cardiac protein expression profiles between the CNH and normoxic groups (Supplementary Tables S5-S6). Principal component analysis (PCA) showed clear separation between groups, indicating substantial proteomic reprogramming under CNH (27% of explained variance; Figure 2A). Analysis of the top differentially expressed (DE) proteins revealed 129 downregulated and 237 upregulated proteins (Supplementary Table S7; Figures 2B,C). Pathway enrichment in CNH hearts (Supplementary Table S8; Figure 2D) indicated regulation of metabolic and stress-response networks. Affected pathways converged on energetic reprogramming (AMPK signaling; glycolysis/gluconeogenesis), lipid/PPAR-associated metabolic flexibility (fatty acid metabolism, peroxisome, glycerophospholipid metabolism), antioxidant systems (glutathione metabolism), and cellular quality control mechanisms (autophagy/lysosome), along with pathways linked to ferroptosis.

Figure 2

Figure 2. Differential expression of proteins in left ventricular samples between continuous normobaric hypoxia (CNH) and normoxic (N) groups. (A) PCA of the expressed proteins showed clear discrimination between the groups (27% of explained variance). (B) Results of the differential expression (DE) anaysis; red dots indicate proteins that met criteria of significance (Supplementary Table S6). (C) Top DE proteins selected by log2FC (Supplementary Table S7). (D) Functional annotation of the results of DE analysis using KEGG database of pathways (Supplementary Table S8).

To integrate proteomic, metabolomic, and lipidomic alterations and assess their coordinated regulation under CNH, a multi-omics analysis was performed using the DIABLO framework. Despite the simple two-group design, this approach confirmed a high degree of cross-omics concordance (average inter-block correlation > 0.98; Supplementary Figure S2A), indicating that proteomic, metabolomic, and lipidomic responses were tightly coupled. Component 1 of the multi-block PLS-DA clearly separated CNH from normoxic hearts (Supplementary Figure S2B) and explained 26%, 34%, and 47% of the total variance in proteomic, metabolomic, and lipidomic datasets, respectively, while Component 2 captured within-group variability. The features contributing most to Component 1 (top 20 per dataset; Figures 3A–C; Supplementary Table S2) defined a coherent biological module rather than independent changes in each omic layer. Proteins with the highest loadings were predominantly associated with hematopoiesis and oxygen transport (GO:0030097) – including HBA1, HBB2, ANK1, SPTA1, SLC4A1, EPB42, BPGM, and HP – and with immune-system processes (GO:0002376) represented by MRC1, LGALS9, and CD1D1. These protein changes were positively correlated with metabolites involved in amino-acid and redox metabolism (e.g., glutathione, aspartate, pantothenic acid) and with membrane lipids such as phosphatidylethanolamines (PE), phosphatidylinositols (PI), and cardiolipins (CL), which participate in mitochondrial and membrane remodeling. Together, the DIABLO analysis quantitatively confirmed that CNH induces a coordinated, cross-omics adaptation encompassing erythrocyte/oxygen transport proteins, metabolic intermediates, and structural lipids. This integrated remodeling reflects the systemic and cardiac adjustments that support energy homeostasis and adaptation to chronic hypoxia (Figure 3D; Supplementary Table S3).

Figure 3

Figure 3. Multi-omics integration of proteins, metabolites and lipids in left ventricular samples. (A) Top proteins discriminating between the experimental groups from the block PLS-DA. Values of each loading vector indicated the importance of a variable in a component. (B) Metabolites and (C) top lipids with the highest contribution to the variation between the tested conditions. (D) Circos plot showing mutual correlations among the top discriminating proteins, metabolites and lipids (Supplementary Table S3).

3.2 Effect of chronic hypoxia on epitranscriptomic regulations

To explore whether adaptation to CNH alters the epitranscriptomic machinery in the heart, we first performed proteomic profiling of m⁶A-related proteins. The analysis identified several m⁶A readers (YTHDF3, EIF3A, EIF3C, EIF3G, HNRNPA2B1, HNRNPC, HNRNPD, PRRC2A) and m⁶A-repelled proteins (G3BP1, ELAVL1, CAPRIN1, RBM42); however, none of the observed changes reached the predefined threshold for statistical significance. Moreover, the writers and erasers of these RNA modifications were not represented among the detected proteins (Figure 4). Given the limited proteomic detection of the enzymatic machinery responsible for m⁶A and m⁶Am modification, we next quantified the key regulatory proteins by Western blotting (Figure 5).

Figure 4

Figure 4. Proteomic profiling of m⁶A regulatory proteins in left ventricular samples between continuous normobaric hypoxia (CNH) and normoxic (N) groups. Heatmap shows relative abundance of m⁶A regulators (readers and m⁶A-repelled proteins) detected by quantitative proteomic analysis. Color scale represents normalized expression changes (log2FC). Due to the low detection of core m⁶A regulatory machinery (writers and erasers) in the proteomic dataset, ten key regulators were further quantified by immunoblotting (see Figure 5).

Figure 5

Figure 5. Effect of continuous normobaric hypoxia (CNH) adaptation on protein levels of m⁶A and m⁶Am regulators in the left ventricular myocardium. Data are presented as medians, with boxes indicating the interquartile range and whiskers representing the minimum and maximum values. Protein levels were normalized to total protein using Ponceau S staining, a method recommended over conventional housekeeping proteins. n = 4; *p < 0.05; **p < 0.01 (t-test). N, normoxia.

CNH strongly upregulated the demethylases ALKBH5 (+106%) and FTO (+77%), whereas methyltransferases METTL3 and PCIF1 remained unchanged and METTL4 decreased (−21%). Among m⁶A readers, all were significantly increased except YTHDC2, which was unaffected: YTHDF1 (+99%), YTHDF2 (+110%), YTHDF3 (+82%), and YTHDC1 (+58%).

Analyses of m⁶A and m⁶Am levels (Figure 6) revealed that in cardiac (LV) total RNA the level of m⁶A was unchanged following CNH, whereas m⁶Am levels increased significantly by approximately 20%.

Figure 6

Figure 6. Effect of continuous normobaric hypoxia (CNH) adaptation on m⁶A (A) and m⁶Am (B) levels in total RNA isolated from the left heart ventricle. Data are presented as medians with boxes and whiskers representing the interquartile range and minimum to maximum values, respectively. n = 4–6; **p < 0.01 (t-test).

4 Discussion

Our data indicate that (i) CNH induces a coordinated remodeling of the cardiac proteome, metabolome, and lipidome consistent with enhanced metabolic flexibility and stress resistance; (ii) this systemic adaptation engages pathways related to energy reprogramming, antioxidant defense, membrane remodeling, and protein quality control; and (iii) CNH is accompanied by profound reorganization of the m⁶A/m⁶Am epitranscriptomic machinery, characterized by upregulation of the demethylases ALKBH5 and FTO and a concerted increase in several m⁶A reader proteins. Collectively, these findings reveal that chronic hypoxic adaptation recruits not only classical metabolic and structural remodeling but also post-transcriptional regulatory layers that may contribute to the maintenance of cardiac homeostasis and the establishment of a cardioprotective phenotype.

Our multi-omics data highlight extensive metabolic and proteomic remodeling underlying adaptation to chronic hypoxia. Indeed, it has been well documented that prolonged hypoxic exposure activates oxygen-sensitive transcriptional regulators, including HIF-1 and PPARs, which collectively promote a metabolic shift toward greater reliance on glucose utilization as more oxygen-efficient substrate compared to fatty acids. This metabolic reprogramming enhances ATP generation efficiency and preserves contractile function under limited oxygen availability (Essop, 2007). In line with this, we observed coordinated regulation of glycolysis, AMPK, and PPAR signaling pathways, together with modulation of the TCA cycle and fatty acid metabolism, indicating enhanced metabolic flexibility. Interestingly, the HIF-1 signaling pathway itself was not among the KEGG-enriched annotations, likely reflecting the adaptive steady state reached after 3 weeks of CNH exposure, when HIF-1 activity becomes attenuated – as previously reported in other tissues (Chavez et al., 2000).

Beyond the well-established HIF-1–mediated metabolic reprogramming, our proteomic data revealed additional adaptive layers that have received little attention in the context of chronic hypoxia. Several proteins associated with mitochondrial quality control and mitophagy (including PINK1, SQSTM1, and GABARAPL2/3) were altered, indicating that the mitophagy machinery is remodeled during long-term hypoxic adaptation. This observation is consistent with our recent findings in a mouse model of intermittent hypobaric hypoxia, where HIF-1α–dependent mitophagy contributed to cardioprotection (Alanova et al., 2024). The convergence of these findings across distinct experimental paradigms suggests that modulation of mitochondrial quality control represents a common feature of chronic hypoxic adaptation, regardless of species or exposure profile.

Concurrent changes in coenzyme Q biosynthetic enzymes (COQ8A, COQ9) and peroxisomal lipid remodeling and antioxidant factors (CAT, ABCD1/3, PEX11B) further indicate reinforcement of mitochondrial redox capacity (Yuan et al., 2021; Guo et al., 2024). Moreover, modulation of RNA-binding and turnover proteins (CNOT1, RBM12, CPEB4, METTL15, CSDC2) together with components of the hexosamine/O-GlcNAc pathway (GFPT1, UAP1, OGA) points to a coordinated post-transcriptional and post-translational remodeling that complements the epitranscriptomic changes observed (Cairns et al., 2022; Dontaine et al., 2022; Qiu et al., 2024; Shi, 2023). These findings suggest that chronic hypoxia triggers a multilayered adaptive program encompassing not only metabolic and structural pathways but also mitochondrial maintenance and RNA metabolism, thereby contributing to the establishment of a durable cardioprotective phenotype.

Given the changes in RNA metabolism–associated proteins and our focus on epitranscriptomic (m⁶A and m⁶Am) regulation as a post-transcriptional layer capable of fine-tuning cellular adaptation, together with the established interconnection between HIF-1 signaling and the m⁶A pathway (Benak et al., 2025), we next explored epitranscriptomic regulation in cardioprotective hypoxic adaptation.

Among demethylases, ALKBH5 and FTO are 2-oxoglutarate/Fe²⁺−dependent dioxygenases whose enzymatic activity requires molecular oxygen; their function may therefore be attenuated under hypoxia (Benak et al., 2024d). The ALKBH5 gene is a direct transcriptional target of HIF-1α, and its induction by hypoxia has been reported in multiple cell types (Thalhammer et al., 2011; Wang et al., 2021). Such upregulation may therefore represent a compensatory mechanism to maintain ALKBH5 activity despite reduced oxygen availability. Consistent with this concept, we observed increased ALKBH5 protein levels in hypoxic rat hearts. By contrast, a study in naked mole rats (Heterocephalus glaber) exposed to acute hypoxia (7% O₂, 24 h) reported decreased cardiac ALKBH5 levels (Ingelson-Filpula et al., 2024), which may reflect species-specific adaptations in an organism renowned for extreme hypoxia tolerance (Kadamani et al., 2024). The regulation of FTO under hypoxia appears more complex. Although hypoxia-responsive elements bound by HIF-1α have been identified in the Fto promoter, HIF-1 has been suggested to suppress rather than activate its transcription (Wang et al., 2024). Accordingly, reduced FTO levels has been observed in hypoxic cardiomyocytes (Deng et al., 2021) as well as in naked mole rat hearts subjected to acute hypoxia (Ingelson-Filpula et al., 2024). In contrast, our data demonstrate increased cardiac FTO protein levels in rats adapted to chronic hypoxia, suggesting that FTO regulation is highly context-dependent, potentially differing with species, tissue type, and the duration or severity of hypoxic exposure.

Similar to demethylases, other m⁶A/m⁶Am regulators are also responsive to hypoxia under certain conditions, including METTL3 (Wang et al., 2021; Yao et al., 2020; Song et al., 2019; Su et al., 2020; Zhao et al., 2021), METTL4 (Hao et al., 2020), YTHDF1-3 (Wang et al., 2021; Hu et al., 2021), and YTHDC1-2 (Wang et al., 2021), as demonstrated in vitro across various cell types. In contrast to ALKBH5 and FTO, which are downstream targets of HIF-1, YTHDC2 has been reported to promote HIF-1α translation (Tanabe et al., 2016). However, in vivo studies on the regulation of m⁶A/m⁶Am machinery under hypoxia remain limited. The previously mentioned study (Ingelson-Filpula et al., 2024) in naked mole rats not only reported decreased cardiac ALKBH5 and FTO levels but also observed downregulation of YTHDF1 – finding that also contrasts with our results, where we observed upregulation of this reader. Additionally, while the latter study reported unchanged levels of METTL3 and YTHDF2-3, our findings demonstrated stable METTL3 levels but upregulated YTHDF2-3 in the hypoxic rat heart. Another study (Shi et al., 2019), which focused on the liver and kidneys, described decreased YTHDF1 levels in highland cattle compared to lowlanders, further supporting the notion that m⁶A/m⁶Am regulators may exhibit tissue-specific and species-specific responses to hypoxia.

Importantly, our findings revealed a counterintuitive increase in FTO protein levels accompanied by elevated m⁶Am levels, a relationship not attributable to changes in the known m⁶Am writers PCIF1 or METTL4. Given that FTO functions as a demethylase, one would typically expect its upregulation to result in decreased m⁶Am abundance. However, m⁶Am remains an understudied modification, and its full regulatory network is not yet well defined. For example, while numerous m⁶A-binding reader proteins have been identified, specific readers for m⁶Am have yet to be discovered. It is therefore plausible that additional, as-yet-uncharacterized regulators or cap-binding proteins may influence m⁶Am dynamics independently of FTO activity. Alternatively, because FTO is a dioxygenase that requires molecular oxygen as a cofactor, its enzymatic activity may be intrinsically limited under hypoxic conditions despite its increased expression. Thus, the observed upregulation of FTO may represent an insufficient compensatory response to maintain m⁶Am homeostasis under sustained low-oxygen stress. However, even upregulation of FTO under hypoxic conditions could prove cardioprotective during reperfusion when oxygen levels are increased and its activity is therefore restored. Future studies will be needed to elucidate the precise mechanistic links between FTO expression, its enzymatic activity under different oxygen levels, and the regulation of m⁶Am, as these questions extend beyond the scope of the present work.

Altogether, these results highlight extensive epitranscriptomic regulation in the hearts of rats adapted to chronic hypoxia, a well-established cardioprotective intervention, and suggest a potential regulatory mechanism underlying the hypoxic adaptation. This observation is consistent with other reports demonstrating epitranscriptomic remodeling in various cardioprotective contexts. For instance, we have previously shown that fasting – a potent endogenous cardioprotective stimulus – is also associated with alterations in m⁶A/m⁶Am machinery and decreased methylation levels (Benak et al., 2024b). Notably, in hearts of fasting rats, we observed increased levels of both demethylases ALKBH5 and FTO, consistent with their known protective effects; however, other components of the regulatory network differed, particularly m⁶A readers, which were predominantly upregulated after adaptation to hypoxia but downregulated following fasting. The upregulation of FTO and decrease in m⁶A levels was also observed on mice intermittent fasting model reported by Xu et al. (2022). These similarities and differences underscore the complexity of m⁶A/m⁶Am regulation in the heart under distinct cardioprotective conditions and highlight the need for a more detailed characterization of its role in the establishment of the cardioprotective phenotype. Nevertheless, while epitranscriptomic remodeling is consistently observed across multiple cardioprotective paradigms, including CNH, the present study is not designed to establish causality, and targeted gain- or loss-of-function approaches will be required to directly define the contribution of specific regulators to hypoxia-induced cardioprotection.

Understanding how oxygen availability modulates RNA methylation and metabolic pathways in the heart may open new therapeutic opportunities. Pharmacological agents mimicking specific aspects of hypoxic adaptation could potentially reproduce the cardioprotective phenotype without the need for physical hypoxic exposure. The therapeutic potential of small-molecule inhibitors targeting m⁶A regulators has already been demonstrated in vitro and in animal models (An and Duan, 2022). Notably, STC-15 – an inhibitor of the m⁶A writer METTL3 – has recently become the first RNA-modifying enzyme inhibitor to enter clinical trials in cancer treatment (NCT05584111) (Medicine, 2025), bringing the concept of therapeutic epitranscriptomic manipulation closer to clinical application. A precise understanding of the role of m⁶A regulation in cardioprotection is therefore of vital importance for the rational design of such strategies.

5 Conclusion

In summary, our integrative multi-omics analysis reveals that chronic normobaric hypoxia induces a coordinated remodeling of cardiac metabolism, protein homeostasis, and epitranscriptomic regulation consistent with a cardioprotective phenotype. Pathway enrichment analyses identified adaptive reprogramming of energy metabolism (AMPK, glycolysis, and PPAR signaling), reinforcement of antioxidant defenses (glutathione metabolism), and structural remodeling of lipid membranes (glycerophospholipid and peroxisomal pathways), accompanied by activation of protein quality control mechanisms (autophagy–lysosome and proteasome systems). Beyond these canonical adaptations, we demonstrate for the first time that chronic hypoxia profoundly alters the m⁶A/m⁶Am regulatory network, characterized by increased abundance of the demethylases ALKBH5 and FTO, upregulation of multiple YTH-domain m⁶A readers, and a selective increase in global m⁶Am levels despite stable m⁶A abundance. This epitranscriptomic remodeling may represent an additional, previously unrecognized layer of hypoxia-induced cardioprotection, linking metabolic flexibility with post-transcriptional control of gene expression. Together, these findings provide a system-level framework for understanding how prolonged hypoxic adaptation reprograms the heart toward enhanced tolerance to ischemic stress.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Ethics statement

The animal study was approved by the Animal Care and Use Committee of the Institute of Physiology of the Czech Academy of Sciences, as well as by the Resort Professional Commission of the CAS for Approval of Projects of Experiments on Animals. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

MH: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft. DB: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – original draft. DS-K: Investigation, Writing – review and editing. KH: Investigation, Writing – review and editing. AtS: Methodology, Investigation, Writing – review and editing. AnS: Investigation, Writing – review and editing. HC: Funding acquisition; Methodology, Writing – review and editing. TK: Formal Analysis, Writing – review and editing. TJ: Investigation, Writing – review and editing. FK: Investigation, Funding acquisition, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Czech Science Foundation (grant number 24-10497S); the Charles University Grant Agency (grant number 298322); the Ministry of Education, Youth and Sports of the Czech Republic [grant Inter-COST LUC24089]; the Operational Programme Johannes Amos Comenius (OP JAC) project RNA for Therapy (reg. number CZ.02.01.01/00/22_008/0004575) co-financed by the EU and the European Research Council Executive Agency (ERCEA) under the European Union’s Horizon Europe Framework Programme for Research and Innovation (grant agreement number 101041374 – StressRNaction).

Acknowledgements

The authors would like to thank the Center of Experimental Medicine of the Institute for Clinical and Experimental Medicine for providing experimental animals (Prof. Ludek Cervenka; Libor Kopkan Ph.D.), acknowledge the Proteomics Service Laboratory at the Institute of Physiology (supported by RVO, ID 67985823) and Institute of Molecular Genetics (supported by RVO, ID 68378050) of the Czech Academy of Sciences and Marek Vrbacky Ph.D. for mass spectrometry proteomics analysis. The authors would like to acknowledge the Metabolomics Core Facility at the Institute of Physiology of the Czech Academy of Sciences and Tomas Cajka Ph.D. for metabolomics and lipidomics profiling.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that Generative AI was used in the creation of this manuscript. ChatGPT 5.1, a large language model developed by OpenAI, was used for language corrections.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2026.1756287/full#supplementary-material

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