Introduction

Globally, more than 140 million people live at altitudes greater than 2500 m above sea level, accounting for 2% of the global population (Aguilar et al. 2018). These high-altitude dwellers are exposed to continuous hypoxia due to the low-pressure effects at high altitudes, mainly in the Bolivian plateau of South America, the Ethiopian plateau of Africa, and the Qinghai-Tibetan plateau of China (Moore 2001). This continuous hypoxia induces a series of physiological changes to maintain oxygen homeostasis in the body. High-altitude polycythemia (HAPC) is a clinical syndrome caused by the loss of the ability to adapt to the environment in people who have lived in high-altitude areas for an extended period or who have migrated from the plains. It is mainly characterized by an overproliferation of red blood cells (León-Velarde et al. 2005), which is not beneficial in the long term and can lead to increased blood viscosity, microcirculatory obstruction, blood clots, extensive organ damage, sleep disturbances, and an increased likelihood of stroke and myocardial infarction in early adulthood (Semenza 2020). It is reported that the prevalence of HAPC in the Qinghai-Tibet Plateau is around 5–18% (Windsor et al. 2007). According to the previous reporting Tibetans may have lower hemoglobin and hematocrit (Wu 2005), higher blood oxygenation, and better labor capacity, all of which are conducive to better adaptation to hypoxic environments, and this adaptation may be related to certain genes, but there are still Tibetans who develop HAPC, and more and more studies have confirmed that adaptation to chronic hypoxic environmental exposures is inextricably linked to several genes in Tibetans, such as endothelial PAS domain protein one gene (EPAS1) (Xu et al. 2015), egg-laying defective nine 1 (EGLN1) (Tashi et al. 2017), catalytic subunit delta gene (PIK3CD), collagen-type IV a3 chain gene (COL4A3) (Fan et al. 2018), and protein Phosphatase 1 Regulatory Inhibitor Subunit 2 (PPP1R2P1) (Gesang et al. 2019).

Abnormal erythropoiesis is the result of genetic background, epigenomic processes, and physiologic responses to chronic hypoxia throughout the life cycle (Villafuerte et al. 2022). It has been proposed that epigenetics shapes adaptation patterns to high altitudes by influencing adaptation potential and phenotypic variability under hypoxia (Julian 2017). Epigenetic regulation includes DNA methylation, post-transcriptional histone modification, and regulation of non-coding RNAs. DNA methylation is the best-studied and most significant method of epigenetic regulation (Lam et al. 2012). This partly heritable (Mendelsohn and Larrick 2013) mechanism regulates the selective transcriptional expression of genes in the presence of methyltransferases, which silence gene expression. A recent study suggests a relationship between Peruvian exposure to high-altitude hypoxia is associated with the methylation of EPAS1 and LINE-1 (Childebayeva et al. 2019). However, little research has been done on the role of epigenetics in HAPC.

In this study, six candidate genes associated with high-altitude adaptation and erythrocytosis, namely, ATP-binding cassette transporter A1 (ABCA1), superoxide dismutase-2 (SOD2), bone morphogenetic protein receptor 2 (BMPR2), serine/threonine protein kinase encoding gene (AKT1), vascular endothelial growth factor receptor 2 (VEGFR2), and transforming growth factor beta (TGF-β), were investigated by quantitative methylation-specific real-time PCR (QMSP). Hypoxia-induced factor-1β (HIF-1β) determines ABCA1 expression under hypoxia in human macrophages (Ugocsai et al. 2010), and ABCA1 is a direct transcriptional target of HIF-1α in human primary renal tubular cells (Safi et al. 2020). Lack of SOD2 in mouse hematopoietic cells results in decreased erythrocyte deformability and increased heme degradation (Mohanty et al. 2013). It is well known that hypoxic condition increases intracellular reactive oxygen species (ROS) levels, and recent studies have elucidated that ROS generated by SOD2 silencing increased HIF-1α expression (Sasabe et al. 2010). The mRNA of AKT1 was higher in the CMS group than in the non-CMS group (Zhao et al. 2017). Furthermore, the PI3K-AKT signaling pathway appears to be involved in the mechanism of reduced erythrocyte apoptosis (Zhang et al. 2022), resulting in increased erythrocyte accumulation and promoting HAPC. VEGFR was higher in participants with versus without excessive erythrocytosis in high-altitude Peru (Painschab et al. 2015). VEGF has been previously shown to regulate critical erythropoietic factors, such as EPO and GATA1 levels (Drogat et al. 2010). HIF-1 accumulation can significantly enhance TGF-β expression (Kushida et al. 2016), and TGF-β has been shown to regulate erythroid differentiation. BMPR2 expression is downregulated in rat hepatocytes under hypoxia (Jiang et al. 2023). The differences in methylation levels of each gene and the relationship with hemoglobin levels were estimated to investigate the effect of methylation on HAPC pathogenesis.

Methods

Study Subjects

A total of 130 Tibetans from the extreme-altitude area of Nagqu, Tibet were included in this study. Subjects were born and settled at altitudes greater than 4500 m above sea level and had families that had been Tibetan for more than two generations. The inclusion criteria were Tibetan ethnicity; age 18–45 years; and no heart or lung diseases. Specimens from the subjects were collected at the Tibet Autonomous Region People's Hospital in July–September 2021 and stored immediately at − 80 °C. Based on the hemoglobin (Hb) levels and criteria of HAPC, the subjects were divided into a HAPC group and a control group. The experiment was approved by the ethics committee of the Tibet Autonomous Region People’s Hospital, and the patients signed an informed consent form.

Exclusion criteria: polycythemia vera; chronic lung diseases, including emphysema, bronchitis, bronchodilatation, alveolar fibrosis, lung cancer, and other serious lung diseases; chronic respiratory diseases or secondary polycythemia due to hypoxemia caused by certain chronic diseases; and serious diseases of the heart, brain, lungs, liver, kidneys, endocrine system, and hematopoietic system.

Routine Blood Indicator Tests

Fasting venous blood was collected from all enrolled individuals, of which EDTA-anticoagulated blood was used to test blood routine indexes by a Sysmex XN-1000 Hematology Analyzer (Sysmex Corporation, Kobe, Japan), and non-anticoagulant blood was left for serum precipitation, centrifuged at a speed of 3500 r/min, and a centrifugation time of 10 min. Serum samples were separated and analyzed for liver and kidney function using the Architect c16000 Automated Biochemical Analysis System (Abbott, USA) and accompanying reagents.

DNA Extraction

Genomic DNA was extracted from whole blood using a QIAamp DNA Blood Mini kit (Qiagen GmbH, Hilden, Germany; cat. no. 51104) according to the manufacturer’s instructions. The concentration of sample DNA was detected with a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Inc., Wilmington, USA). Purified DNA was eluted in AE buffer (Qiagen GmbH) and stored at − 20 °C for use in subsequent experiments.

Sodium Bisulfite Modification

The bisulfite conversion method is the most commonly used technique to convert unmethylated cytosines into uracil, whereas methylated cytosines remain unchanged. DNA extracted from blood was modified with EZ DNA Methylation-Gold Kit (Zymo Research, Los Angeles, USA; Cat. No. D5005 & D5006). As a positive control, 1 μg of human HCT116 DKO methylated DNA (Zymo Research, Irvine, California, USA) was sulfite converted and stored in 20 μL of sterile double-distilled water.

Primer and Probe Design

The QMSP assay uses two primers that specifically bind to fully methylated target gene DNA and a Taqman probe, each labeled with two fluorescent dyes, with the 5′ end (FAM or VIC) as the excitation group and the 3′ end (BHQ1 or BHQ2) as the bursting group. All primers and probes attached to methylated sequences were designed by Beacon Designer™ (version 8.13; Premier Biosoft International, Palo Alto, CA, USA), as shown in supplementary Table 1. The UCSC database (http://genome.ucsc.edu) and NCBI were used to search for suitable promoter regions of the target genes, each about 2 kb upstream of the transcription start site (see supplementary files). The internal reference gene, β-actin (ACTB), was used to calibrate the sample DNA content. The primers and probes were synthesized and purchased from Sangon Biotech (Shanghai, China).

Real-Time Quantitative Methylation-Specific Polymerase Chain Reaction

The EpiTect® MethyLight PCR+ROX Vial Kit (Qiagen GmbH; cat. No. 59496) was used for QMSP experiments and each reaction contained 0.4 µM each of forward and reverse primers; 0.2 µM probe; 2 µL of transformed DNA, and 25 µL 2 × EpiTect Methylight Master Mix. Amplification in the LightCycler 480 real-time PCR system (Roche Biochemical, Mannheim, Germany) was run under the following conditions: 5 min of activation PCR step at 95 °C, 45 cycles of annealing at 95 °C for 15 s, and extension at 60 °C for 60 s. Each plate included sample DNA, positive quality control, and water as blank. QMSP results were calculated using the percentage of methylated reference (PMR), which was described to represent the methylation level (Quillien et al. 2012, Liu et al. 2014, Pérez-Carbonell et al. 2010, Andresen et al. 2015, Pan et al. 2018):

$${\text{PMR}}{\mkern 1mu} = {\mkern 1mu} {2^{ - \Delta \Delta {\text{Ct}}}}{\mkern 1mu} \times {\mkern 1mu} 100\% ,{\mkern 1mu} \Delta \Delta {\text{Ct}}{\mkern 1mu} = {\mkern 1mu} \Delta {\text{C}}{{\text{t}}_{{\text{target gene}}}} - \Delta {\text{C}}{{\text{t}}_{{\text{ACTB}}}}{\mkern 1mu} = {\mkern 1mu} \left[ {{\text{sample DNA }}\left( {{\text{C}}{{\text{t}}_{{\text{target gene}}}} - {\text{C}}{{\text{t}}_{{\text{ACTB}}}}} \right) - {\text{completely transformed DNA }}\left( {{\text{C}}{{\text{t}}_{{\text{target gene}}}} - {\text{C}}{{\text{t}}_{{\text{ACTB}}}}} \right)} \right]$$

Statistical Analysis

Data were processed with R software (R Core Team 2018), and the packages lme4 (Bates et al. 2015) and lmerTest (Kuznetsova et al. 2017) were used for statistical analysis. Images were generated using GraphPad Prism 8.0 (GraphPad Software, San Diego, CA). Depending on the type of data, the Pearson chi-square test, Wilcoxon rank-sum test, and Fisher exact test were used to assess the differences between the two groups. The significance level for all statistical tests was set at p < 0.05. Linear mixed models were used to explore the correlation of the methylation levels of each gene with hemoglobin (Hb), Percutaneous arterial oxygen saturation (SpO2), and body mass index (BMI), and the model equation was as follows:

Target gene (% methylation) ~ Hb+altitude+(1|age)+(1|sex)+(1|groups)+SpO2+BMI, data = JIAJI, REML = T). “Target gene” refers to ABCA1, AKT1, SOD2, VEGFR2, TGFβ, or BMPR2.

Results

The characteristics of the participants are shown in Table 1; the study included 130 hereditary Tibetans at extreme altitudes, divided into 46 HAPC patients and 84 healthy controls according to the diagnostic criteria for HAPC, with no difference in gender distribution between the two groups (71% male vs. 65% male, p = 0.5). The average ages were 34 (29, 36) in control and 36 (31, 40) in HAPC, respectively, and the average altitude was greater than 4500 m above sea level in these two groups. There were significant differences in SpO2 and BMI between the two groups (p < 0.001), SpO2 decreased with the increase of hemoglobin, whereas BMI increased with it. The levels of RBC, Hb, and HCT were significantly higher in the HAPC group than in the control group (p < 0.001). The liver function indicators alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TB), and indirect bilirubin (IB) were significantly increased in the HAPC group (p < 0.01). The renal function indicators, uric acid (UA) increased in the HAPC group (p < 0.001).

Table 1 Participant Characteristics and Methylation changes of Tibetan HAPC patients and control (Naqu, 4622–4920 m)
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The methylation levels of TGF-β and BMPR2 were significantly different (P < 0.01), while there was no difference in ABCA1, SOD2, AKT1, and VEGFR2. The PMR of TGF-β was 0.28% (0.19%, 0.39%) in the control group and 0.37% (0.31%, 0.44%) in the HAPC group, respectively (p = 0.001). The methylation level of BMPR2 was 62% (44%, 100%) in control and 100% (69%, 122%) in HAPC, respectively (Table 1), see Fig. 1. Otherwise, methylation levels of BMPR2 and TGF genes were also differentially expressed in different hemoglobin level groups. The PMR of TGF-β increased with the increase in hemoglobin: 0.29%, 0.34%, and 0.46%, in Hb-L (Hb = 130–174 g/L), Hb-M (Hb = 175–209 g/L), and Hb-H (Hb > 210 g/L) groups, respectively. The methylation levels of TGF-β in the Hb-H group were significantly higher than those in Hb-L (p < 0.01) and Hb-M (p < 0.05). The methylation level of BMPR2 also increased gradually with the increase of hemoglobin: 70.27%, 79.50%, and 99.52%, in Hb-L, Hb-M, and Hb-H groups, respectively (see Supplementary Table 2), and the methylation level of BMPR2 in the Hb-H group was significantly higher than that in the Hb-L (p < 0.01) and Hb-M groups (p < 0.05).

Fig. 1
figure 1

The relative methylation level (percentage of methylated reference (PMR), %) of BMPR2, ABCA1, SOD2, AKT1, VEGFR, and TGF-β between the control and HAPC groups. *p < 0.05; **p < 0.01

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Linear mixed model analysis showed that the correlation between the hemoglobin and methylation of ABCA1, TGF-β, and BMPR2 was statistically significant (p < 0.05). The methylation levels of ABCA1 were negatively associated with hemoglobin level (β = − 0.179, p = 0.023). The methylation levels of BMPR2 and TGF-β were positively related to hemoglobin levels, and methylation levels of the BMPR2 gene correlated more strongly with hemoglobin than those of the TGF-β (BMPR2: β = 0.534, p = 0.003; TGF-β: β = 0.003, p = 0.003), whereas there was no significant correlation with SpO2 and BMI (p > 0.05), see Table 2.

Table 2 Significant linear mixed effects models
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Discussion

The earliest inhabitants of the Qinghai-Tibet Plateau arrived 30,000 to 40,000 years ago (Zhang et al. 2018a, b). With the immigration of more and more Han Chinese settlers, about 3 million people in Tibet now live in areas more than 3000 m above sea level. Although most Tibetans have adapted to high-altitude life, some still develop chronic altitude diseases, such as polycythemia and pulmonary hypertension. These are potentially fatal and can only be relieved by transportation to lower altitudes. However, the specific molecular mechanism of HAPC is unclear, and the effect of methylation on HAPC has not been explored. In this study, we determined that the methylation levels of two genes, BMPR2 and TGF-β, differed significantly between HAPC and control. However, HAPC may be caused by multiple aspects, such as genes and epigenetics, so differential DNA methylation of TGF-β and BMPR2 is not the sole cause of HAPC for Tibetans at high altitude.

Both hypermethylation of SOD2 and reduced enzyme activity contribute to the development of pulmonary hypertension (Hernandez-Saavedra et al. 2017). In erythropoiesis, PI3K/AKT promotes proliferation and inhibits apoptosis of progenitor erythrocytes (Missiroli et al. 2009). AKT mRNA expression increases in CMS, and the rate of erythrocyte apoptosis negatively correlates with AKT mRNA expression (Zhao et al. 2017). VEGF is a well-known target of HIF-1. By binding to its receptor VEGFR2, it increases vascular permeability, promotes endothelial cell proliferation, induces vascular neovascularization, improves local tissue blood supply, and thus improves hypoxia (Ferrara 2004). An association between ABCA1 DNA methylation levels and coronary artery disease (CAD) has also been proposed (Liang et al. 2013). ABCA1 gene methylation levels did not differ between the HAPC and control groups but were negatively correlated with hemoglobin and a further increase in sample size may be needed to clarify ABCA1 gene methylation expression in HAPC patients.

TGF-β is involved in inflammation, angiogenesis, wound healing, and immune cell function. Elevated levels of TGF-β in the pulmonary arteries of chronic low-pressure hypoxic rats may promote pulmonary hypertension and pulmonary remodeling (Zhang et al. 2018a, b), suggesting that TGF-β, which is abnormally expressed under hypoxia, may be involved in the pathogenesis of certain hypoxia-related diseases. TGF-β also negatively regulates the differentiation and maturation of erythrocytes (Parisi et al. 2021), and TGF-β regulates the erythropoietin gene under hypoxia (Sánchez-Elsner et al. 2004). Under hypoxic conditions, the accumulation of HIF-1 significantly enhances the expression of TGF-β, and ROS can stimulate the expression of TGF-β1 (Zhou et al. 2009). In our study, the level of TGF-β methylation differed among the patient and control groups, but the degree of methylation was not high overall; the correlation coefficient with Hb was low (β = 0.003, p = 0.003).

BMPR2 encodes a type II receptor of bone morphogenetic protein (BMP), mice with a BMPR2 mutation are more likely to develop hypoxic pulmonary hypertension (Hautefort et al. 2019). BMP is also involved in stress-induced erythropoiesis (Liang and Ghaffari 2016). BMP4 plays a dominant role in promoting vascular remodeling in response to exposure to hypoxia (Frank et al. 2005). Hypoxia can also upregulate BMP-2 in osteoblasts by activating the HIF-1a and ILK/Akt/mTOR signaling pathways (Tseng et al. 2010). The BMP-SMAD signaling pathway regulates iron metabolism via hepcidin. Mutations or inactivation of the genes encoding BMP ligands or BMP receptors in this pathway can cause dysregulation of hepcidin, leading to iron-related disorders, such as hemochromatosis and iron-refractory iron deficiency anemia (Xiao et al. 2020). Hepcidin is a major regulator of systemic iron homeostasis (Wang and Babitt 2019) and can directly block the ability of its receptor protein, ferroprotein (FPN), to transport iron (Aschemeyer et al. 2018). A recent study (Liu et al. 2018) found that compared with the low-altitude group, the serum hepcidin level of healthy immigrants in the high-altitude area decreased, and the serum hepcidin level of the high-altitude HAPC patients was lower than that of the high-altitude control group. Hepcidin levels also decreased with altitude within two days and were strongly correlated with changes in serum ferritin levels (Piperno et al. 2011). However, the regulation of hepcidin under hypoxic conditions remains unclear. BMP6 can negatively regulate iron content by regulating hepcidin, thereby affecting RBCs. As a binding site of BMP-2/6, BMPR2 may be involved in the regulation of hepcidin. BMPR2 knockout mice show hemoglobin deposition (Mayeur et al. 2014), hypoxia, or ineffective RBC production will promote the binding of ERFE to BMP to inhibit the level of hepcidin, resulting in increased RBC. In this study, the methylation level of the BMPR2 gene increased in HAPC, and the correlation coefficient between BMPR2 methylation level and Hb was β = 0.534, p = 0.003, indicating that the low expression of the BMPR2 gene may be related to the generation of Hb. Under hypoxic conditions, it may be that the expression of HAMP, a gene encoding hepcidin, is downregulated, whereas FPN is upregulated, leading to the release of free Fe, thereby promoting the overproduction of erythrocytes.

The present study has some limitations. First, although this study is the first to investigate the relationship between gene methylation levels and HAPC, only six genes were involved, many other genes in hypoxia or erythropoiesis development were not included. Second, we failed to detect the levels of proteins encoded by TGF-β and BMPR2, and downstream molecules in peripheral blood. Future experiments could combine gene and protein levels to verify the diagnostic value of TGF-β and BMPR2 methylation levels in HAPC.

Conclusion

BMPR2 and TGF-β promoter methylation levels differed significantly in HAPC and control of Tibetans from extreme altitudes. There was a connection between the methylation levels of ABCA1, BMPR2, and TGF-β and the occurrence of HAPC.