Subjects and data collection
The study was approved by No. 20180308 of the Xiangya Hospital Ethics Review Committee. From October 2018 to January 2019, 51 HC and 55 asthma patients were chosen from the Respiratory Department and Physical Examination Center of Xiangya Hospital, China. FEV1/FVC ratio <0.7 and FEV1% <70% was defined as the presence of asthma. The inclusive standards for the patient group were between the age of 40 and 70 with a clear diagnosis of asthma (according to the criteria of 2019 Global Strategy for Asthma Management and Prevention) and no other respiratory and cardiovascular diseases, diabetes [19]. The healthy control group had no differences in age and gender without asthma or other organic mental diseases, including smoking and non-smoking controls. Quality control methods were strictly enforced.
After obtaining the written informed consent from each subject, we collected questionnaire information (general condition, smoking history and other respiratory diseases), pulmonary function testing and peripheral blood samples. For our analysis, pulmonary function parameters were adopted including forced expiratory volume in one second as percentage of predicted volume (FEV1%), the spirometric values of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), peak expiratory force (PEF) and forced expiratory flow (FEF). Certified staff performed all interviews and examinations. Moreover, feedback on work quality would be regularly provided to field staff during the data collection process, and secondary training would be conducted when necessary.
Sample collection
A total of 106 whole blood samples were collected from the enrolled 51 HCs and 55 asthma patients, respectively. Then,collected peripheral blood was placed into 5 ml EDTA anticoagulation tubes and transferred to a centrifuge tube. After adding 2 volumes of erythrocyte lysate and lysing for 5 minutes, peripheral blood cells were pelleted by centrifugation and stored at -80℃.
RNA extraction and quantitative RT-PCR
Total mRNA was purified from peripheral blood cells using Trizol (Invitrogen) and quantified by an ultraviolet spectrophotometer (Thermo Fisher Scientific, USA) [20]. 1μg RNA was reverse transcribed into cDNA using Reverse Transcriptase Kit (Qiagen, Netherlands) accordance to the manufacturer’s instructions [21]. Then, quantitative RT-PCR was executed using SYBR® Premix Ex TaqTM II system (TaKaRa, Japan) with the CFX96 TouchTM Real-Time PCR Detection System (Bio‐Rad, USA). 1μl of the reverse-transcript was added to a 30 μl PCR mixture for 40 cycles. Each cycle included 93℃ for 30s and 54℃ for 60s. By the comparison between the copy numbers of target gene and β-actin,the normalization of mRNA expression data for sample-to-sample variability in RNA input, RNA quality and reverse transcription efficiency was completed. Primer sequences were described in Table 1.
DNA Extraction, Bisulfite Treatment, Methylation Array Methods
A commercially available kit (TIANGEN Biotech, Beijing, China) was used to extract genomic DNA from whole blood according to previous publications [22]. Genesky Biotechnologies Inc. Shanghai performed bisulfite processing, methylation library construction, high-throughput sequencing and quality control [23]. CpG islands located between 2K upstream of the gene transcription start site and 1K downstream of the first exon were selected to measure methylation level. 18 CpG islands from the 9 screened aging-related genes were selected (2 from AREG, 2 from ATG3, 1 from E2F1, 3 from FOXO3, 1 from HDAC1, 3 from MMP2, 1 from NUF2, 3 from TGFB1 and 2 from TP53) according to our previous publications [18]. Then, bisulfite modification of DNA sample, methylation library construction and MethTarget were performed [18]. 856 CpG sites from 9 distinguishingly expressed aging-related genes in the methylation assay were detected. We only selected the original data with a sequencing quality value of Q>40 (basic sequencing error rate <0.1%), and the methylation percentage of each CpG site was presented.
Statistical analysis
The characteristic data of all recruited asthma patients and HCs were showed as Mean ± SD, p-value < 0.05, analyzed by unpaired T test. T test and nonparametric test (Mann-Whitney U test) were used to analyze the mRNA expression and the methylation array of AREG, ATG3, E2F1, FOXO3, HDAC1, MMP2, NUF2, TGFB1 and TP53. We used the Benjamin Hochberg method to control the false discovery rate (FDR). The selection of distinguishingly expressed CpG sites was performed by Logistic regression analysis, with latent risk factors of age and gender [24]. The correlation between the percentage of methylation of candidate CpG sites and successive variables for instance FEV1%, FVC, FEV1 and PEF was assessed by Pearson’s correlation or Spearman’s correlation. ROC analysis was obtained to elucidate the accuracy of candidate DMSs or methylation change rate s in predicting asthma. For each candidate DMS, the optimal cutoff value for predicting asthma and corresponding sensitivity and specificity were defined by the maximum Youden index value (sensitivity + specificity-1) [25]. The methylation percentage of candidate DMSs or the methylation status (change or not change) were used for PCA to identify asthma. For each candidate DMS, the change in methylation status was defined by its optimal threshold [26]. The methylation change rate in each sample mainly referred to the probability that the methylation status of the candidate DMSs changed. The statistical analyses were implemented using SPSS version 22.0 (IBM Corporation, Armonk, NY, USA). A two-tailed p-value < 0.05 was considered statistically significant, **** p < 0.0001; * p < 0.05.