1.1 Sample collection: Plasma samples from 3 patients with AAA and 3 healthy people were provided by the First Affiliated Hospital of Zhengzhou University. All patients with AAA were confirmed by CTA that the diameter of the abdominal aorta was larger than 3 cm (Table 1). healthy group without AAA was selected as the control group. The blood is collected into the EDTA anticoagulant tube and gently mixed to ensure exposure to the wall of the tube coated with EDTA.
Table 1. Clinical characteristics of patients with AAA and organ donors
|
Patient 1
|
Patient 2
|
Patient 3
|
Control 1
|
Control 2
|
Control 3
|
Sex
|
Male
|
Male
|
Male
|
Female
|
Male
|
Female
|
Hypertension
|
-
|
-
|
+
|
-
|
-
|
-
|
Diabetes
|
+
|
-
|
-
|
-
|
-
|
-
|
Dyslipidemia
|
-
|
-
|
-
|
-
|
-
|
-
|
CAD
|
+
|
-
|
-
|
-
|
-
|
-
|
COPD
|
+
|
-
|
-
|
-
|
-
|
-
|
Renal dysfunction
|
-
|
-
|
-
|
-
|
-
|
-
|
MAAA(mm)
|
58.2
|
39.7
|
53
|
-
|
-
|
-
|
Abbreviations: CAD coronary artery disease, COPD chronic obstructive pulmonary disease, MAAA maximum abdominal aortic diameter.
1.2 Exosome separation: Firstly, the plasma sample was centrifuged at 2000 g at room temperature for 20 min, the residual cells and fragments were removed. Then, the supernatant was absorbed into the new tube, centrifuged at 10000 g at room temperature for 20 min again. According to the agreement, the supernatant was fully mixed with 1/3 volume Ribo Exosome Isolation Reagent(RIBOBIO,China),standing at 4 ℃ for 30 min, followed by 15000 g centrifugation for 2 min to remove the supernatant. Phosphate buffered saline was used to re-suspend exosome particles.
1.3 Exosome identification: Using Zetasizer Nano-ZS (Malvern Panalytical, UK), the molecular diameter distribution determined by Nanoparticle Tracking Analysis (NTA) software is mainly composed of 20-200 nm particles. Fluorescent direct labeling CD63 and CD81 antibody (BD, USA) were used for staining, and unstained exosome was labeled as NC as negative control. Accuri C6 flow cytomenter (BD, USA ) was used for flow cytometry analysis (> 83%), which proved that the isolated EV contained abundant exosomes. The results are shown in Fig 1. Characterization of EV particle diameter is shown in Table 2.
Table 2. Characterization of EVs particle diameter
|
Sample
|
Average diameter(nm)
|
48.89
|
Polydispersity index(DPI)
|
0.306
|
Major peak of particle diameter (nm)
|
74
|
Percentage of 20-20nm Diameter (%)
|
89.5
|
Polydispersity index (PDI) is a dimensionless value that represents the distribution of particle size. PDI values of 0.08–0.7 indicate moderate dispersion system and optimum application scope of algorithm.
1.4 RNA separation: Total RNA was extracted from exocrine by Trizol reagent (Invitrogen, Life Technologies).
1.5 RNA detection: According to the manufacturer's agreement, the concentration of RNA was quantified by NanoDrop2000 spectrophotometer (Thermo Science, USA). The OD260/280 ratio of samples is between 1.8 and 2.1, which is acceptable. RNA integrity and genomic DNA contamination were detected by denaturing agarose gel electrophoresis. The sampled 2 ul was analyzed by Aligent2200 bioanalyzer (Agilent, USA). The RNA map of Agilent bioanalyzer showed RNA peaks around 25 nt and 200 nt, but not 18 s and 28 s rRNA peaks. This indicates that the plasma exosomal RNA is mainly small RNA. The result is shown in Fig 2.
2.6. cDNA library construction and high-throughput sequencing: The 3 ' and 5' connectors were linked to RNA, including primers. Then reverse transcription and PCR amplification were performed. Amplification conditions: 95 °C for 15 second, then 94 °C for 15 second, 55 °C for 30 second, and 70 °C for 34 second.
3. Analysis of high-throughput sequencing data
The clean reads was obtained by removing the joint sequence, length < 17 nt and low quality reads. Clean reads were compared with human genome by Burrow-Wheeler Aligner software. Comparing and annotating clean data with a variety of RNA databases11. Using Rfam 11.0 for yRNA, rRNA, snRNA, snoRNA and tRNA. MiRBase 21 for miRNA, piRNABank for piRNA12.
Compared with the entire reference sequence of miRBase 21, the number of miRNA expressions in each sample was obtained and standardized as number of mapped reads per million clean reads (RPM)13. The analysis of differential miRNA expression is an independent hypothesis statistical test for thousands of miRNAs. This multiple test has the problem of high false positive. In order to highlight the difference in expression, the P value needs to be corrected. We use DEGseq to correct the P value to get the Q value. The lower the Q value is, the more significant the difference in miRNA expression is14. The significant difference of miRNA expression was determined by DEGseq software. Setting P < 0.05, Q<0.05, |log 2 (FoldChange) | ≥ 1. Drawing the volcano map(Fig 3) and heat map(Fig 4) of the overall miRNA expression differences among the samples.
4. GO and KEEG enrichment analysis of predicted miRNA targets.
Targets of miRNA with significant differences were predicted by TargetScan, miRDB, miRTarBase and miRWalk software, and the intersections were selected15,16. The targets were annotated in the KEGG biological pathway database, and the biological pathway enrichment analysis of targets was carried out by using Fisher Exact Test, with the threshold of P<0.0517. According to GO gene annotation, all genes of this species were selected as background genes, and hypergeometric method was used to calculate high frequency annotation(P < 0.05)18.