2.1. Study Participants and Ethics approval
Sixty hypertension patients were recruited from the Third People’s Hospital of Kunming City, including 30 hypertension patients without drug use (HBP) and 30 hypertension patients with intravenous drug use (HBP-DU). All participants had no infectious diseases and inflammation. The information of participants is shown in Supplemental Table 1.
2.2. Sample collection and preparation
Five milliliters of peripheral blood was collected from 60 hypertension patients using vacuum blood collection tubes containing EDTA anticoagulant. Blood samples were stored at -4°C until for use.
2.3. Metabolites extraction
Metabolites were extracted with 50% methanol buffer. Briefly, 20 µL peripheral blood was mixed with 120 µL of precooled 50% methanol and vortexed for 1 min. The above mixture was incubated at room temperature (approximately 25℃) for 10 min. Then stored the mixture overnight at -20°C. After centrifugation at 4,000 g for 20 min, the supernatant was transferred into new 96‐well plates. The pre-processed samples were stored at -80°C and prepared for the LC-MS analysis. In addition, pooled QC samples were also prepared by combining 10 µL of each extraction mixture.
2.4. LC-MS/MS-based untargeted metabolite identification
A liquid chromatography (UPLC) system (SCIEX, UK) coupled with high-resolution tandem mass spectrometer TripleTOF5600plus (SCIEX, UK) was applied to detect metabolites. An ACQUITY UPLC T3 column (100mm*2.1mm, 1.8µm, Waters, UK) was used for the reversed-phase separation. The column oven was maintained at 35°C. The flow rate was 0.4 ml/min and the mobile phase consisted of solvent A (water, 0.1% formic acid) and solvent B (Acetonitrile, 0.1% formic acid). Gradient elution conditions were set as follows: 0ཞ0.5 min, 5% B; 0.5ཞ7 min, 5–100% B; 7 ~ 8 min, 100% B; 8ཞ8.1 min, 100–5% B; 8.1ཞ10 min, 5% B. The injection volume for each sample was 4 µL.
To identify metabolites species eluted from the column, the Q-TOF was operated in both positive and negative ion modes. The Ionspray voltage floating was 5000 V and ‐ 4500V for positive and negative ion mode, respectively. The mass spectrometry data was acquired in IDA mode. The TOF mass was from 60 to 1200 Da. The survey scans were acquired in 150 ms, and the total cycle time was fixed to 0.56 s. Four-time bins were summed for each scan at a pulse frequency value of 11 kHz through monitoring of the 40 GHz multichannel TDC detector with four‐anode/channel detection. Dynamic exclusion was set for 4 s. During the acquisition, the mass accuracy was calibrated for every 20 samples. Furthermore, to evaluate the stability of the LC‐MS during the whole acquisition, pooled QC samples (pool of all samples) were acquired after every 10 samples.
Raw data of metabolomics was converted into mzXML format and then processed using the XCMS, CAMERA, and metaX toolbox in R software. Each ion was identified by the comprehensive information of retention time and m/z. The intensity of each peak was recorded and a three-dimensional matrix containing arbitrarily assigned peak indices (retention time-m/z pairs), sample names (observations), and ion intensity information (variables) were generated. Then the information was matched to the public database including KEGG and HMDB, which were used to annotate the metabolites by matching the exact molecular mass data (m/z) to those from the database within a threshold of 10 ppm. Principal component analysis (PCA) was performed to detect outliers and batch effects using the pre‐processed dataset. Student’s tests and FDR (Benjamini–Hochberg) were used for the differential metabolites (DMs) selection. To identify more specific differences between the HBP and HBP-DU groups, we conducted the supervised PLS‐DA using metaX to variables. The VIP cut‐off value of 1.0 was set to select important features.
2.5. RNA extraction and library construction
DNA library was constructed according to the workflow as described previously [3]. Total RNA was isolated from peripheral blood using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the procedure. The RNA was quantified using NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA) and Bioanalyzer 2100 (Agilent, CA, USA). Dynabeads Oligo (dT) 25-61005 (Thermo Fisher, CA, USA) was used for purifying Poly (A) RNA, and then the poly(A) RNA was fragmented into small pieces using Magnesium RNA Fragmentation Module (NEB, USA). Then the cleaved RNA fragments were reverse-transcribed by SuperScript™ II Reverse Transcriptase (Invitrogen, USA), which were next used to synthesize U-labeled second-stranded DNAs with DNA polymerase I (NEB, USA), RNase H (NEB, USA), and dUTP Solution (Thermo Fisher, cat. R0133, USA). Then an A-base is added to the blunt ends of each strand for ligating the adapter with a T-base overhang. After being treated with the UDG enzyme (NEB, USA), the ligated products are amplified with PCR under conditions: 95°C for 3 min; 8 cycles of denaturation at 98°C for 15 s, annealing at 60°C for 15 s, and extension at 72°C for 30 s; and then final extension at 72°C for 5 min. At last, high-throughput sequencing was performed with the 2 × 150 bp paired-end sequencing (PE150) on an Illumina Novaseq™ 6000 (LC-Bio Technology, Hangzhou, China).
2.6. Identification of Differentially Expressed mRNA
Fastp (https://github.com/OpenGene/fastp) was applied to obtain the clean reads by removing the reads that contained adaptor contamination, low-quality bases, and undetermined bases. HISAT2 (https://ccb.jhu.edu/software/hisat2) was used to map the clean reads to the reference genome of Homo sapiens GRCh38. The mapped clean reads were assembled using StringTie (https://ccb.jhu.edu/software/stringtie). Then, all transcriptomes from all samples were merged to reconstruct a comprehensive transcriptome using gffcompare (https://github.com/gpertea/gffcompare/). StringTie was used for estimating mRNAs expression by calculating FPKM (FPKM = [total_exon_fragments / mapped_reads(millions)×exon_length(kB)]). The differentially expressed genes (DEGs) were selected with |fold change| > 2 and parametric F-test comparing nested linear models (p < 0.05) by R package edgeR (https://bioconductor.org/packages/release/bioc/html/edgeR.html).
2.7. Weighted Gene Co-Expression Network Analysis (WGCNA)
To screen hub genes that were significantly associated with HBP-DU, we conducted WGCNA using R with the “WGCNA_1.70-3” package15 under the parameter of FPKM ≥ 1, Pearson correlation coefficient = 0.8 and soft threshold (power) = 6, the threshold for merging modules = 0.5. Hub genes were selected under the condition of gene significance > 0.1 and module membership (kME) > 0.96.
2.8. Functional Enrichment Analysis
Reactome (https://reactome.org/) was used for analyzing the function of DEGs and hub genes. The threshold of Reactome enrichment was considered significant at p < 0.05. Gene Set Enrichment Analysis (GSEA) was applied to identify a priori-defined set of genes that showed differences in HBP and HBP-DU groups.
2.9. Correlation between DEGs and DMs
Spearman correlation analysis was performed for correlation between DEGs and DMs using R 3.6.1 with the stats package. Complete-linkage clustering was employed for computing hierarchical clustering among DEGs and DMs.
2.10. Abundance Analysis of Immune Cells
ImmuCellAI [15] was used to compare the abundance of 24 immune cell subsets between the HBP and HBP-DU groups.
2.11. Reverse Transcription Quantitative PCR
The expression DEGs were validated by quantitative reverse transcription PCR (RT-qPCR). We selected two DEGs to validated the results of RNA sequencing. The primer sequences are PI3-F: CACGGGAGTTCCTGTTAAAGG, PI3-R: TCTTTCAAGCAGCGGTTAGGG; FOS-F: GGGGCAAGGTGGAACAGTTAT, FOS-R: CCGCTTGGAGTGTATCAGTCA. Total RNA was extracted using TriQuick Reagent (Solarbio Science, Beijing, China). Reverse Transcription was performed using RT First-Strand Synthesis Kit (Servicebio, China). The reverse transcription mixture (15 µL) contained Oligo (dT)18 Primer (1 µL, 50 µM), Random Hexamer primer (1 µL, 50 µM), total RNA (2 µL, 200 ng/µL), and RNase-free ddH2O (11 uL). After incubated at 65°C for 5 min, 5 × Reaction Buffer (4 µL) and RT Enzym Mix (1 µL) were added. cDNAs were synthesized at 25°C for 5 min, 42°C for 30 min, and 85°C for 5 s.
The qPCR mixture (20 µL) contained 2 × SuperReal PreMix Plus (10 µL), Primer F/R (0.6 µL, 10 µM), cDNAs (2 µL, 40 ng), 50 × Rox Reference Dye (0.6 µL), ddH2O (6.2 µL). The qPCR was performed at the conditions of 95°C for 15 min; 40 cycles of 95°C for 30 s, and 60°C for 60 s. The dissolution curve was drawn at 95°C for 60 s, 55°C for 30 s, and 95°C for 30 s. The internal reference gene was GAPDH. Each experiment was performed in triplicate.
2.12. Statistical Analysis
Statistical analyses were performed by using SPSS 20.0. All data were expressed as mean ± SEM. The student’s t-test was used for analyzing RT-qPCR results, p < 0.05 was considered statistically significant. GraphPad Prism 8.0 was used to prepare the graphs.