Sevoflurane with Low Concentration Decrease DNA Methylation on Chronic Obstructive Pulmonary Disease (COPD)-Related Gene Promoter in COPD Rat

Abstract

Chronic obstructive pulmonary disease (COPD) is a difficult-to-cure disease that mainly affects the respiratory system. Inhaled anesthetic drug such as sevoflurane plays a controversial role in COPD by different concentration, but the underlying epigenetic mechanism remains unclear. Here, we prepared lipopolysaccharide (LPS)-induced COPD rat model, and isolated Alveolar type II (ATII) cells. We mainly focused DNA methylation on the promoter of COPD-related genes including Sftpa1, Napsa, Ca2, Sfta2, Lamp3, Wif1, Pgc, and Etv5. We observed COPD rat treated by sevoflurane with low (0.5%) and high (2%) concentrations displayed an opposite DNA methylation pattern. These six genes' promoter were all hypomethylated by 0.5% sevoflurane whereas hypermethylated by 2% sevoflurane, accompanied with the opposite transcriptional activity. We further verified that the DNMT1 binding ability contributed to DNA methylation these six genes' promoter. Moreover, we also captured DNMT1 and identified REC8 meiotic recombination protein (REC8) as the specific binding protein only existed in ATII cells treated with 0.5% sevoflurane rather than 2% and control. The binding ability of REC8 on these target genes' promoter showed highly positive correlation with DNMT1. In summary, we uncovered a potential epigenetic role of sevoflurane with low concentration in ATII cells of COPD that may help us deeply understand the pathogenesis and treatment mechanism of inhaled anesthesia drugs in COPD via a dose-dependent manner.

Keywords:

• COPD
• sevoflurane
• DNA methylation
• REC8
• DNMT1

Introduction

Chronic obstructive pulmonary disease (COPD) is a difficult-to-cure disease that mainly affects the respiratory system. About 3 million people die from COPD worldwide each year, accounting for about 5% of global deaths. Meanwhile, the number of COPD patients is increasing globally and it is expected that by 2030, the disease will become the third leading cause of death worldwide [1]. COPD includes two types, chronic bronchitis and emphysema. The lungs of patients with this disease become increasingly stiff, and breathing becomes difficult. Even light exercise can cause shortness of breath and fatigue. This disease is one of the deadliest diseases globally, but it is preventable in most cases. The most common cause of COPD is smoking, but it also includes air pollution, chemicals particulate matter, wood smoke. Incomplete reversibility of airflow limitation, inflammation, excessive mucus secretion, and bronchial mucosal epithelial lesions constitute the primary pathological basis of the disease [2]. However, people who do not smoke and have less contact with pollutants can also develop this disease. To date, there is no cure for COPD, and methods such as using respirators, oxygen therapy, and speech therapy can only slow the progression of COPD.

DNA methylation is a chemical modification that adds a methyl group to DNA. It is one of the most extensively studied epigenetic modifications, playing important roles not only in gene expression and genetic stability but also as a biomarker for risk assessment and prognosis in various diseases in clinical diagnostics. However, multiple clinical studies suggest that DNA methylation in peripheral blood cells is not associated with COPD [3, 4]. On the other hand, genome-wide DNA methylation in lung tissues have been studied to support the COPD pathogenesis [5, 6]. Most environmental risk factors such as smoking, particulate matter (PM2.5), wood smoke exposure can cause the aberrant methylation thereby affect the related gene transcription in COPD [7, 8].

Some studies have shown that inhaled anesthetic drugs may have negative effects on COPD. These drugs may cause a series of lung inflammation and damage, which can worsen the symptoms of COPD. Inhaled anesthetic drugs, such as sevoflurane and isoflurane, have been shown to affect inflammation-related transcription factors and cytokines, including NF-κB, TNF-α, and IL-6, which are widely believed to play a key role in the formation and development of COPD. Therefore, it is crucial to choose safe and effective anesthetic drugs for COPD patients. Anesthesiologists and clinical doctors need to weigh the risks and benefits to choose the most appropriate drugs for patients during surgery and anesthesia process, and timely detect and solve related problems during monitoring. Nevertheless, it is not quite clear whether anesthetic drugs, like those environmental risk factors, can affect COPD through DNA methylation. Therefore, in current study, we establish the lipopolysaccharide (LPS)-induced COPD rat model, and examined the effect of sevoflurane with low and high concentrations on COPD, and further investigated the underlying mechanism of DNA methylation behind it. Our results will help people understand the impact of inhaled anesthetics on COPD surgical patients. In addition, this will also supplement the epigenetic regulation mechanism of COPD by environmental interference, and provide some useful evidence and clues for the treatment of COPD.

Materials and methods

Animals study

LPS-induced COPD rat model was established as previously described [9]. Twenty sprague-dawley (SD) rats (male, aged 10-week, 300–400 g body weight) were randomly divided five individuals in each group as negative control (NC), LPS, LPS with 0.5% sevoflurane (LPSL), LPS with 2% sevoflurane (LPSH). Briefly, after the rats were anesthetized by 1% pentobarbital sodium intraperitoneally, 0.2 mg LPS generated from Escherichia coli 055: B5 (MedChemExpress, #93572-42-0, China) was administrated via tracheal injection for six times (twice a week). Rats were given an additional 0.5% and 2% sevoflurane inhalation within a 12 × 14 × 20 cm³ Plexiglas chamber via internal gas circulation using a peristaltic pump (Runze Fluid, LM60A YZ1515X, China) [10] for 2 h. NC group was injected with the same volume of saline.

On the 22nd day, the pulmonary ventilatory function of the rats were measured by AniRes2005 Animal Lung Function Analysis System (Beilanbo Technology, China). The rats were anesthetized by 1% pentobarbital sodium intraperitoneally, and the limbs and head of the rats were fixed. After the neck skin was sheared, the skin, muscle layer and fascia of the neck were cut longitudinally, the anterior cervical muscle group and other tissues were bluntly separated, and the trachea was exposed. A T-shaped incision was made at the proximal head of the trachea, and a tracheal catheter with an inner diameter of 2 mm was used. After fixation, the cotton thread was transferred to the body tracing box, and the ventilator was connected to the tracheal junction. The changes of expiratory resistance (Re), inspiratory resistance (Ri) and airway compliance (Cldyn) of the rats were recorded.

After lung function assay, rats were sacrificed and lung tissues were isolated to conduct the consequent experiments. The studies were conducted in accordance with the Animal Component of Research Protocol guidelines at Fudan University.

Cell sorting and cytometric assay

10 ml of PBS and 1.5 ml of Dispase (Aladdin, N128693, USA) were perfused into the right ventricle of rats, and then infused through the tracheal catheter, following adding 0.3 ml 1% low-melting agarose (Beyotime, ST105, China) pre-warmed to 42 °C. Lung tissues were harvested and incubated at room temperature in 2.5 ml of Dispase for 45 min, then transferred into 5 ml Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F12) (ThermoFisher Scientific, #21331020, USA) supplemented with 0.04 mg/ml DNase I (ThermoFisher Scientific, #18047019), and 1% penicillin/streptomycin (ThermoFisher Scientific, #15070063). Lung tissues were then dissected into individual lobes, and the fragments were sequentially transferred to a new culture dish with 8 ml of fresh medium. The tissue was gently separated with forceps. The resulting cell suspension was homogenized and transferred to a 50 ml conical tube. The cell suspension was filtered through nylon meshes with pore sizes of 100, 20, and 10 μm and centrifuged at 200× g for 10 min at 15 °C. The supernatant was discarded, and the cell pellets were resuspended in medium. The single-cell lung suspension from three rats was incubated with mouse anti-rat CD45-PE (BD Biosciences, #554878, USA) and rat anti-mouse CD31-PE (BD Biosciences, #555027) on ice. The cells were washed and resuspended by medium to the final concentration of 1 × 10⁷/ml. The cells were filtered through 100, 40, and 35 μm cell strainers and sorted by FACS Aria III (BD Biosciences). Cells were excluded based on their characteristics of FSC-H to FSC-A and FSC-W to FSC-A to exclude cell doublets. Alveolar type II (ATII) cells were identified as a population negative for CD45/CD31 and high in their own fluorescence (FITC channel). Cell sorting was performed using a nozzle with a diameter of 85 μm and a 45 psi sheath fluid pressure.

100 μl anticoagulant peripheral blood from caudal vein was incubated with primary antibodies of 5 μl anti-rat CD4, FITC (ThermoFisher Scientific, #11-0040-82), anti-rat IL-17A, APC (ThermoFisher Scientific, #17-7177-81) and anti-rat CD25, APC (ThermoFisher Scientific, #17-0390-82). Canto II (BD Biosciences) work station was used to assess the Th17 (CD4⁺/IL-17A⁺) and Treg (CD4⁺/CD25⁺) cell population.

Hematoxylin-eosin (H&E) staining

After fixation, the lung tissues were embedded in paraffin and sliced into 20 μm sections. The steps of deparaffinization, staining, dehydration, clearance, and sealing were referred to the instructions of H&E staining kit (Beyotime, C0105S). Under the view of Olympus BX-51 microscope (Olympus Corporation, Tokyo, Japan) at 400× magnification, Image Pro-plus 7.0 (Media Cybernetics, USA) was used to count the number of inflammation cell infiltration area (select 3 nonconsecutive slides, and select 20 independent fields of view on each slide), and calculate the average area of inflammatory infiltrates.

Immunohistochemistry (IHC) assay

The fixation, embedding in paraffin and sectioning were same with H&E staining. Primary antibodies of IL-17A (Novus, NBP3-13294, USA, 1: 200) and IL-10 (Novus, AF519, USA, 1: 500) were used, followed by development of DAB horseradish peroxidase color (Beyotime, P0202). Images were captured under the view of Olympus BX-51 microscope at 400× magnification, and Image Pro-plus 7.0 was used to count the number of brown positive staining and blue nucleus staining (select six nonconsecutive slides and ten independent fields of view on each slide by three analyzers). H-score is calculated by ∑ (Pi × I)/100. Pi denotes the percentage of stained cells and I denotes the intensity of the staining ranging from 0 to 3, which represent 0: no staining, 1: faint yellow, 2: claybank and 3: dark claybank.

ELISA

The production of IL-10 and IL-17A in rat serum by tail tip blood collection were analyzed by Elabscience ELISA kit (E-EL-R0016c, E-EL-R0566c) according to the manufacturer's instruction, OD values were detected by Multiskan FC microplate reader (ThermoFisher Scientific).

Western blot (WB) assay

Total protein of tissues or cells was extracted using RIPA lysis buffer (Beyotime, P0013B) with 1× Protease and Phosphatase Inhibitor Cocktail (Beyotime, P1048). The BCA protein assay kit (Beyotime, P0010S) was used to determine the protein concentration in the supernatant. 10 μg total protein was uploaded on sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) with 15% separation gel and transferred onto polyvinylidene fluoride (PVDF) membranes. The condition of primary antibodies of DNMT1 (Novus, NB100-56519, 1: 2,000), DNMT3A (Novus, NB120-13888, 1: 2,000), DNMT3B (Novus, NB300-516, 1: 2,000), REC8 meiotic recombination protein (REC8) (Proteintech, 10793-1-AP, 1: 2,000) and GAPDH (Novus, NB100-56875, 1: 5,000) incubating overnight as well as secondary antibody of goat anti-rabbit (Novus, VC002, 1: 5,000) or horse anti-mouse (Novus, MP-7500-NB, 1: 5,000) IgG-HRP incubating 2-h was carried out. BeyoECL Plus (Beyotime, P0018S) was used to develop the protein band. Image Pro-plus 7.0 was used to assess the protein expression levels.

Dot plot assay

The extracted genomic DNA from the lung tissues by Universal Genomic DNA Purification Mini Spin　kit　(Beyotime, D0063) in accordance with the manufacturer's guidelines was spotted on PVDF membrane in 2 μl and subjected to dot-blot assay at 2-fold dilutions (0, 1, 2, 4, and 8 ng). The spots were air-dried at room temperature and then incubated with 5-methylcytosine (Novus, NBP2-42814, 1: 2,000) overnight at 4 °C with a shaking incubator. The membrane was then incubated with horse anti-mouse IgG-HRP (1:20,000) at room temperature on next day. Finally, the membrane was developed by BeyoECL Plus at room temperature in the dark for 5 min. Pro-plus 7.0 was used to assess DNA methylation levels.

Methylated DNA immunoprecipitation (MeDIP)

MeDIP assay was conducted on 3 μg DNA that had been extracted from ATII cells. Methylated segments were obtained from the fragmented genomic DNA using 100 μg 5-methylcytosine antibody and 10 μl magnetic protein A beads (Merck, #16-663, USA). The enriched DNA sample was then eluted in nuclease-free water by magnetic capture and simple washing steps. Standard qPCR was applied to the DNA. The untreated fragmented DNA acted as the input control to determine the relative methylation level (MeDIP/input).

Chromatin immunoprecipitation (ChIP)

5 × 10⁷ ATII cells were lysed using RIPA lysis buffer with 1× Protease and Phosphatase Inhibitor Cocktail. Target protein binding segments were obtained from the fragmented genomic DNA using 800 μg primary antibodies against DNMT1, DNMT3A, DNMT3B or REC8 and 10 μl magnetic protein A beads. Follow-up operations were consistent with MeDIP.

Quantitative polymerase chain reaction (qPCR)

Template cDNA was used to perform PCR within a 20 μl reaction mixture containing specific primers (Sangon Biotech, China) using BeyoFast™ SYBR Green qPCR Mix (2×) (Beyotime, D7260). Primers sequence were listed below:

Sftpa1_promoter: F: 5′-ATAGTTTTGTTTGTTCGGGTGGTTTGCTTTCCTTCC-3′, R: 5′-CGGAGCTGGGGACCGAACCCAGGGCCTTGTGCTTCCTAGGTA-3′.

Sftpa1: F: 5′-GGGGATAGTAGCCATGTCACTGTGTTCTTTGG-3′, R: 5′-CCTCAGGAGTCCTCGGGACAGCAATGTTG-3′.

Napsa_promoter: F: 5′-AGGTTTAGTGAGGGACTCAATAACAAGGTGA-3′, R: 5′-GTACCCTTTAGGGTGGCAGTGGTGCAAAGGTTCA-3′;

Napsa: F: 5′-ACCCTGGACACAGGATCTTCAGCCCACTGTATGGATG-3′, R: 5′-GCTCAGGATTCCACTCAGCCGCCCGGTCCCAT-3′.

Car2_promoter: F: 5′-GTGGCCGGAGCCGCGCTGTGAGGGC-3′, R: 5′-GGCGGACGGAGGTGACAAGCAGAGGTGACAGG-3′; Car2: F: 5′-CCCTGTGGACATTGACACCGGGACTGC-3′, R: 5′-GCCCCAGTGAAAGTGAAACTGGATC-3′.

Sfta2_promoter: F: 5′-ACCTGAAGGACCCAGGCTTAGGTGGGG-3′, R: 5′-CACCAGTGTTGGGGGGCACCAAGAGACCA-3′; Sfta2: F: 5′-GCCACCATGGAGCCTTCGATGTGCCTCTTCCTCCTC-3′, R: 5′-GTGGTGTGGGTGGCCCTTTATGATGAAGGGTG-3′.

Lamp3_promoter: F: 5′-CTGAAATTAATCCTTTCCTCTTCTAGTTGCTTTGA-3′, R: 5′-GATTTAGCTCAGTGGTAGAGTGCTTGCCTA-3′; Lamp3: F: 5′-AATGGCAATAAAGGAGTTTAAAAAGAATTTC-3′, R: 5′-ATCTATGACCTTTGAACATTATATTAAT-3′.

Wif1_promoter: F: 5′-TTTCAACTTCCCAGGAGAGCGC-3′, R: 5′-TACCCGGGAGACGGTGCTGGCTGGCTGG-3′; Wif1: F: 5′-CGGATGCAGGGCAGCCGCCAGAGGA-3′, R: 5′-GACAGTTGGGTCTGCCATGATGCCTT-3′.

Pgc_promoter: F: 5′-CTGAACTCTAGCTCCCAGAAGCTTA-3′, R: 5′-TTGAAGAAGGGGCCATGATGGTTGGTTCCTC-3′; Pgc: F: 5′-TCCTGGTCCTTTTCGACACCGGCTCC-3′, R: 5′-AGAGTGTCATAGCCAAAGAAG-3′.

Etv5_promoter: F: 5′-ACGTCCTTACATTCATTATATA-3′, R: 5′-CTGCCCATCACCTCTTGTTTACCCTG-3′; Etv5: F: 5′-CAAGGAACTACTCCATGCTGAAGC-3′, R: 5′-CTGAAAATCTGGGACAAACTGCTCATC-3′.

Immunoprecipitation (IP)

5 × 10⁷ ATII cells were lysed using RIPA lysis buffer with 1× Protease and Phosphatase Inhibitor Cocktail. To rule out DNA interference with protein interactions, genomic DNA still needed to be fragmented. Primary antibodies against DNMT1 or REC8 were used to capture each other. The washing steps were similar with ChIP. Final proteins were dissolved into SDS protein loading buffer and denatured on 100 °C for 10 min, then processed to WB for detecting interaction.

Statistical analysis

Data of one sample was collected multiple times by biological or technical repetition, and presented as the format of mean ± standard deviation. Student's t-test was used to measure the significant differences between groups by pvalue less than 0.05.

Results

Sevoflurane exacerbated LPS-induced inflammatory response of COPD in low concentration

Firstly, we induced a COPD model rat using LPS and additionally treated them with different concentrations of sevoflurane (See methods). The lung function assay indicated that the resistance of expiratory and inspiratory increased whereas airway compliance decreased significantly by low concentration of sevoflurane (Figure 1a). Furthermore, we found that the proportion of Th17 (CD4⁺/IL-17A⁺) and Treg (CD4⁺/FOXP3⁺/CD25⁺) cell subsets in CD4⁺ T cells within peripheral blood was increased significantly in LPSL compared to LPS, but was no difference between LPSH and LPS (Figure 1b, c). Meanwhile, H&E staining of lung tissue revealed the presence of a large number of inflammatory cells invasion (Figure 1d). IHC analysis showed a significant reduction of IL-10 and up-regulation of IL-17A in lung tissues of LPSL compared to LPS and LPSH (Figure 1e, f). Additionally, the expression of IL-10 and IL-17A in serum were consistent with the variation tendency of IHC in LPSL model (Figure 1g). This suggests that inhaled sevoflurane in low concentration can enlarge the inflammatory effect of COPD.

Figure 1. Inflammatory response of lung tissues in COPD rat model. (a) Lung function assay shows the expiratory resistance (Re), inspiratory resistance (Ri) and airway compliance (Cldyn) in LPSL model. (b, c) Flowcytometric assay show the populations of Th17 (CD4⁺/IL-17A⁺) (b) and Treg (CD4⁺/FOXP3⁺/CD25⁺) (c) CD4 cells in peripheral blood. (d) H&E staining show the inflammatory cells invasion in lung tissues of COPD compared to NC with 400× magnification. Lymphocytes presenting the small cytoplasm, large nucleus and blue staining are highlighted by red arrows. (e, f) IHC shows the positive staining of IL-10 (e) and IL-17A (f) in different samples with 400× magnification. (g) ELISA shows the expression of IL-10 and IL-17A in serum of COPD model. “*” and “Δ” mean the statistical significance with p less than 0.05 compared to NC and LPS group respectively. Abbr eviation: NC, negative control; LPS, LPS-induced COPD model; LPSL, LPS treated with 0.5% sevoflurane; LPSH, LPS treated with 2% sevoflurane.

Sevoflurane decreased DNA methylation level in ATII cells

Next, we studied DNA methylation of ATII cells affected by sevoflurane. ATII cells with different treatment were isolated using flow cytometry (Figure 2a), and conducted dot plot assay. We observed that LPSL could significantly increase whereas LPSH could decrease the global genomic methylation level of ATII cells compared to LPS (Figure 2b). Nevertheless, a comparison with previous study on single-cell sequencing data of COPD [11] revealed that the expression of genes in ATII cells, such as Sftpa1, Napsa, Ca2, Sfta2, Lamp3, Wif1, Pgc, and Etv5 were highly expressed in LPSL compared to LPS and LPSH (Figure 2c). In turn, the promoter of these genes presenting the varying degrees of hypomethylation in LPSL compared to LPS and LPSH in current system (Figure 2d) were well-fitted to the negative correlation with methylation (r = −0.554, p = 0.0031) (Figure 2e). These results indicated that sevoflurane with low concentration could specifically reduce the DNA methylation level on promoters of COPD related genes.

Figure 2. DNA methylation in ATII cells of COPD. (a) ATII cells were sorted based on positive autofluorescence (FITC) and negative expression of CD45 and CD31 (upper panels). cells were re-analyzed the expression of CD45, CD31 and intracellular CD74 before (bottom left panel) and after (bottom right panel) sorting. (b) Dot plot assay (upper panel) and line chart (bottom panel) show the overall genomic DNA methylation of different samples. “Δ” and “ΔΔ” mean the statistical significance with p less than 0.05 and 0.01 compared to LPS group. (c-d) Bar charts show the mRNA relative levels by qPCR (c) and DNA methylation levels on promoter by MeDIP-qPCR (d) of genes including Sftpa1, Napsa, Ca2, Sfta2, Lamp3, Wif1, Pgc, and Etv5. (e) Scatter diagram shows the Pearson correlation analysis between mRNA and DNA methylation levels.

Sevoflurane excluded the enrichment of DNMT1 on gene promoter

Next, we moved on to the mechanism underlying the target gene methylation changes caused by sevoflurane. As is well-known, DNMT family plays an essential role in regulating DNA methylation. Therefore, we firstly started with the expression of DNMT1, DNMT3A and DNMT3B using WB assay, but found no significant differences (Figure 3a). Unexpectedly, ChIP-qPCR suggested the differences of DNMT1 enrichment on these target gene promoters, in which DNMT1 binding ability was remarkably weakened particularly in LPSL compared to LPS and LPSH (Figure 3b). Whereas the occupancies of DNMT3A and DNMT3B on these target genes showed no differences (Figure 3c–e).

Figure 3. DNMT1 binding ability on gene promoter affected by sevoflurane. (a) The expression of DNMTs and REC8 by WB assay in different samples. (b-e) Bar charts show the occupancies of DNMT1 (b), DNMT3A (c) and DNMT3b (d) on gene promoter in different samples by ChIP-qPCR. IgG (e) used as a negative control.

Based on it, we performed IP to pull down DNMT1 to study the binding proteome on it. It was notable that a band at 80 kDa specially appeared in ATII cells of LPSL (Figure 4a), and REC8 was identified with the highest confidence by MS (Figure 4b). IP-WB assay verified the specific interaction between DNMT1 and REC8 in NC and LPSL (Figure 4c), and regular WB also confirmed that sevoflurane with low concentration could recruit the robust abundance of REC8 by DNMT1. It was interesting that LPS actually did not affect the expression of REC8 (Figure 3a). Consistently, the enrichment changes of REC8 on these target gene promoters positively correlated with DNMT1 (r = 0.9, p = 3.5e-12) (Figure 4d, e). We determined that REC8 might modulate the recruitment of DNMT1 on the target genes related COPD for DNA methylation in ATII cells in vivo.

Figure 4. Interaction between REC8 and DNMT1. (a) Coomassie bright blue stain shows the binding proteome of DNMT1. Red arrow highlights the specific band only exists in LPSL. (b) Secondary mass spectrum shows the partial peptide “TEVTPP” of the interested band, which is identified as REC8. (c) IP-WB assay shows the interaction between REC8 and DNMT1. (d) Bar charts show the occupancies of REC8 on gene promoter in different samples by ChIP-qPCR. IgG is referred as Figure 3e. (e) Scatter diagram shows the Pearson correlation analysis between enrichment of REC8 and DNMT1 on target genes.

Discussion

The impact of inhaling anesthetics on COPD is highly controversial in clinical practice. On one hand, inhaling anesthetics can inhibit the ciliary activity of respiratory epithelial cells, impairing the excretion of secretions, leading to small airway obstruction and alveolar collapse. Anesthetics like isoflurane also stimulate the respiratory tract, leading to an increase in airway secretions by inhibiting hypoxia-induced pulmonary vascular constriction (HPV). On the other hand, sevoflurane can help reduce the release of airway inflammatory factors, improve airway remodeling, regulate oxidative stress balance, lower airway resistance, promote bronchial dilation, and play a protective role [12, 13]. Essentially, the impact of this difference mainly depends on the concentration and duration of inhalation of anesthetic drugs [14, 15]. Most current literature has indicated that alleviative effects on almost all lung related diseases including acute lung ischemia-reperfusion injury [16], respiratory distress syndrome [17], lung function deterioration [18], cancer [19], lung transplantation [20], allergic airway inflammation [21] and so forth. However, very few cellular and molecular studies have been conducted on the adverse effects of sevoflurane on lung tissue. Therefore, this study focuses on the impact of low concentrations of sevoflurane on COPD. Our observations display the elevated overall genomic DNA methylation in COPD, which is consistent with previous study [6].

The main new finding in this article is that the effect of low-concentration sevoflurane on COPD is completely opposite to that of high-concentration, which has never been reported in previous studies. It is generally believed that different concentrations of sevoflurane can cause different gasping reactions in the brainstem, and the reaction caused by low-concentration sevoflurane is similar to the respiratory pattern under hypoxic conditions [22]. This means that a specific concentration of sevoflurane can cause changes in respiratory patterns. For COPD patients, forcing respiratory muscles to work excessively during respiratory muscle fatigue, increased bronchial resistance, and decreased pulmonary alveolar elasticity will only further exacerbate COPD symptoms. This is one of the reasons why we believe that low-concentration sevoflurane is not beneficial for COPD patients.

Moreover, sevoflurane has been reported to affect cell status of proliferation, invasion, apoptosis, cell damage and injury as well as cognitive impairment via multiple signaling pathways such as HIF-1α/MIF/AMPK [23], NMDR/NMNAT1/2 [24], Ras/Raf/MEK/ERK [25], PI3K/Akt/mTOR [26], JNK/p38/MAPK [27], however, through a dose-dependent manner [28]. Here, we speculate that sevoflurane causing different DNA methylation pattern on promoter of genes related to COPD is also relied on concentration of sevoflurane. Although the underlying mechanism on regulating DNA methylation remains unclear, we have determined that the interaction between REC8 and DNMT1 is indeed affected by different concentrations of sevoflurane, and further found the reduced expression of REC8 by low concentration of sevoflurane. Rec8 as a subunit of the meiotic cohesin complex, which is conserved in a wide range of eukaryotes, plays a role in modulating chromosomal architecture during the pairing and recombination of homologous chromosomes in meiosis [29]. REC8 can support the cohesion complex to affect the histone modification [30] and DNA methylation [31]. In this system, we have confirmed that REC8 closely correlates with DNMT1 at the COPD-related genes, which may be as a breakthrough to elucidate the DNA methylation mechanism of genes in COPD. By the current presence, we believe that it is very likely that REC8 inactivates DNMT1 function, and their interaction is more likely to be free protein complexes, rather than binding to exert methylation regulation on DNA. However, in order to further clarify the epigenetic relationship between them, we will perform REC8 knockdown in future studies, observe the overall methylation and the enrichment changes of DNMT1 on target genes, and formally clarify the causal relationship between REC8 and DNMT1.

Conclusion

Overall, our data uncovers that treatment of sevoflurane with low concentration can result in DNA hypomethylation and activated transcription in COPD-related genes in ATII cells. ATII cells treated by sevoflurane with low concentration leads to a specific interaction between REC8 and DNMT1, and further weaken the DNMT1 binding to these target genes for DNA methylation loss.

Ethics statement

All animal experiments were approved by the Ethics Committee of Qingpu Branch of Zhongshan, Fudan University in accordance with the National Institutes of Health Guide for Care and Use of Laboratory Animals (NIH Publications, No. 8023, revised 1978).

Acknowledgements

This work is supported by Shanghai Qingpu District Health Committee Scientific Research Project (W2021-17).

Disclosure statement

The authors have no conflicts of interest to declare.

Data availability statement

All data generated or analyzed during this study are included in this published article.

Additional information

Funding

This work is supported by Shanghai Qingpu District Health Committee Scientific Research Project (W2021-17).