Research Article | Open Access
Microarray Analysis and Detection of MicroRNAs Associated with Chronic Thromboembolic Pulmonary Hypertension
The aim of this study was to understand the importance of chronic thromboembolic pulmonary hypertension- (CTEPH-) associated microRNAs (miRNAs). miRNAs differentially expressed in CTEPH samples compared with control samples were identified, and the target genes were predicted. The target genes of the key differentially expressed miRNAs were analyzed, and functional enrichment analyses were carried out. Finally, the miRNAs were detected using RT-PCR. Among the downregulated miRNAs, MiR-3148 regulated the most target genes and was significantly enriched in pathways in cancer, glioma, and ErbB signaling pathway. Furthermore, the number of target genes coregulated by miR-3148 and other miRNAs was the most. AR (androgen receptor), a target gene of hsa-miR-3148, was enriched in pathways in cancer. PRKCA (Protein Kinase C Alpha), also a target gene of hsa-miR-3148, was enriched in 15 of 16 KEGG pathways, such as pathways in cancer, glioma, and ErbB signaling pathway. In addition, the RT-PCR results showed that the expression of hsa-miR-3148 in CTEPH samples was significantly lower than that in control samples (). MiR-3148 may play an important role in the development of CTEPH. The key mechanisms for this miRNA may be hsa-miR-3148-AR-pathways in cancer or hsa-miR-3148-PRKCA-pathways in cancer/glioma/ErbB signaling pathway.
Chronic thromboembolic pulmonary hypertension (CTEPH), a complication of acute pulmonary embolism, is characterized by the persistence of a thromboembolic obstruction of the pulmonary arteries by organized tissue and the presence of variable small vessel arteriopathy . In 2015 ESC (European Society of Cardiology)/ERS (European Respiratory Society) Guidelines for the diagnosis and treatment of pulmonary hypertension (PH), CTEPH is classified as the fourth types of PH . It is reported that CTEPH has a cumulative incidence of 0.1–9.1% within the first 2 years after a symptomatic pulmonary embolism event . Risk factors for CTEPH include circulating antiphospholipid antibodies or lupus anticoagulant, increased factor VIII, non-O blood groups, and chronic inflammatory diseases . The survival of CTEPH patients is poor in the absence of specific surgical or medical treatment . Therefore, there is an urgent need for effective treatments for CTEPH.
With the rapid development of bioinformatics, high-throughput microarray data analysis plays an important role in the study of the molecular mechanism of disease. Pathways enriched by differentially expressed genes and interactions between genes can provide theoretical basis for the mechanisms of disease occurrence and development. MicroRNAs (miRNA), small noncoding RNAs, are differentially expressed in many cardiovascular diseases, including pulmonary hypertension (PH) . A previous study indicated that levels of miR-125a were increased in the lung tissues of hypoxic animals that developed PH . Courboulin et al. suggested that miR-204 plays a significant role in decreasing proliferation, vascular remodeling, and pulmonary artery blood pressure in PH . Furthermore, the fibrinogen alpha gene regulated by miR-759 is associated with a susceptibility to CTEPH . Wang et al. suggested that miRNA let-7d may play important roles in the pathogenesis of CTEPH . Therefore, miRNAs may be important biological molecules to understand the mechanisms of CTEPH. However, the miRNAs associated with CTEPH have not been fully characterized.
To understand the miRNAs associated with CTEPH, we carried out microarray analysis and detection of miRNAs. Firstly, miRNAs differentially expressed in CTEPH samples compared control samples were identified, and the target genes of these differentially expressed miRNAs were predicted. Then, the target genes of the key differentially expressed miRNA were analyzed, and functional enrichment analyses were carried out. Finally, the miRNAs were detected using RT-PCR.
2. Materials and Methods
2.1. miRNAs Expression Profile Data
Peripheral blood of CTEPH patients (4 samples in CTEPH group) in Beijing Chao-Yang Hospital, Capital Medical University, and healthy volunteer (5 samples in control group) with routine physical examination in physical examination center from March to April 2016 were collected. This study was approved by the Ethics Committee of Beijing Chao-Yang Hospital, Capital Medical University. The requirement to obtain informed written consent was waived. The information about the patients was shown in Table 1.
|For smoking, we did not investigate this information for control group, but there was no correction between smoking and CTEPH according to previous studies. For BMI, we did not investigate this information for control group. 160039K-1, 160039K-2, 160039K-3, 160039L-1, 160039J-6, 160039J-7, 160039J-8, 160039J-3, and 160039J-10 were chip number.|
Total RNAs of the samples were extracted following the manufacturer’s protocol by the RNAprep Pure Blood Kit (Tiangen Biotech Co., Ltd., Beijing, China), and then RNA was purified with mirVana™ miRNA Isolation Kit (AM1561). Quantification was performed by using spectrophotometer or Qubit, and quality control was carried out by using agarose gel electrophoresis or Agilent 2100. Total RNA was labeled by poly(A) polymerase addition using the Genisphere FlashTag HSR kit following the instructions of the manufacturer instructions (Genisphere, Hatfield, PA). RNA was hybridized to the Affymetrix miRNA array. Chips were washed and stained by using Affymetrix® GeneChip® Command Console® Software (AGCC). After scanning, fluorescent scan images were saved in .DAT files with AGCC. A total of 9 human blood samples (4 samples: 160039k_1, 160039k_2, 160039k_3, and 160039L_1 in the CTEPH group; 5 samples: 160039J_6, 160039J_7, 160039J_8, 160039J_9, and 160039J_10 in the control group) were included in the Affymetrix miRNA chip.
2.2. Screening for Differentially Expressed miRNAs
Data preprocessing including robust multiarray averaging (RMA) normalization, discrimination of probe signal, and integration of probe set signal was performed by using Expression Console package provided by Affymetrix. SAM (significance analysis of microarray) R software package  with values ≤ 0.05 and was used for the identification of differentially expressed miRNAs.
2.3. Prediction Analysis for Target Genes of the Differentially Expressed miRNAs
Combined with the results of the miRWalk, Microt4, miRanda, mirbridge, miRDB, miRMap, miRNAMap, Pictar2, PITA, RNA22, RNAhybrid, and Targetscan databases, prediction analysis to determine the target genes of the differentially expressed miRNAswas carried out using miRWalk2.0 (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk2/) [10, 11]. Prediction results greater than six were regarded as being the result of regulation of a target gene by the miRNA, and differentially expressed miRNA-target gene pairs were obtained.
2.4. Functional Enrichment Analysis for Differentially Expressed miRNAs
The number of target genes regulated by differentially expressed miRNAs was counted, and KEGG pathway enrichment analysis was performed for the top 5 differentially expressed miRNAs by using clusterProfiler in R package . was set as the threshold values.
2.5. Target Genes Coregulated by Differentially Expressed miRNAs Analysis
The coregulation network of two miRNAs was constructed using the coregulated target genes of the two miRNAs. The networks for these microRNAs were constructed using Cytoscape software .
2.6. The Network Construction for Target Genes Regulated by Differentially Expressed miRNAs
The target genes regulated by more differentially expressed miRNA were regarded as key target genes. The top 100 target genes regulated by more miRNAs were obtained and the network was constructed with these target genes and miRNAs.
2.7. Functional Enrichment Analysis for Target Genes of Key miRNAs
GO  and Kyoto Encyclopedia of Genes and Genomes (KEGG)  pathway enrichment analysis were carried out for the target genes regulated by key miRNAs using the DAVID (Version 6.8, https://david-d.ncifcrf.gov/) online tool (classification stringency = medium) . was set as the threshold values.
2.8. Detection of miRNAs Using RT-PCR
A total of 11 RNA samples (CTEPH group: K-1, K-2, K-3, K-4, and SN6 and control group: J6, J7, J8, J9, J10, and MN-N2) were used for the detection of miRNAs. Based on previous studies and our experience, we measured the expression of hsa-miR-3148. The primers for the miRNA are shown in Table 2.
Poly(A) was added to the 3′ end of the miRNA as follows: firstly, 1 μl 10x EPAP Reaction Buffer, 1 μl 25 mM MnCl2, 1 μl 10 mM ATP, 6.5 μl total RNA, and 0.5 μl Escherichia coli poly(A) polymerase were added to a precooled RNase-free reaction tube with a total volume of 10 μl. The prepared reaction solution was gently mixed using transferpettor, and the reaction was performed at 37°C for 60 min after transient centrifugation. The obtained solution was used for a subsequent experiment or transiently preserved at −20°C (long-term storage at −80°C).
The reverse transcription reaction mixture was prepared as follows: firstly, 3 μl RT-Primer (10 μM) and 1 μl dNTP Mixture (10 mM each) were added to the 10 μl prepared reaction solution and then RNase-free water was added up to 20 μl. The denaturation reaction was performed at 65°C for 5 min. The mixture was then precooled on ice. Then, 4 μl 5x PrimeScript II Buffer, 0.5 μl (20 U) RNase Inhibitor (40 U/μl), 1 μl (200 U) PrimeScript II RTase (200 U/μl), and 0.5 μl RNase-free dH2O were added to 14 μl of the above denaturation reaction solution, and the solution was mixed using a transferpettor. Then, after transient centrifugation, the reverse transcription reaction was performed at 42°C for 60 min and 95°C for 5 min and then cooled on ice .
Then, the qPCR reaction solution was prepared according to the following components: 10 μl SYBR Premix EX Taq (2x), 1 μl forward primer 10 μM, 1 μl reverse primer 10 μM, and 8 μl cDNA. The qPCR reaction was performed using the following steps: 50°C for 3 min, 40 cycles of 95°C for 3 min, 95 for 10 s, and 60°C for 30 s. Finally melt curve analysis was carried out in 60–95°C using increments of 0.5°C per 10 s.
All results are presented as the mean ± SEM and presented in tables. SPSS22.0 was used for the statistical analyses, and GraphPad Prism 5 (GraphPad Software, San Diego, CA) was used for mapping. Values of and were set as a significant difference and an extremely significant difference.
3.1. Screening of Differentially Expressed miRNA
A total of 46 (24 upregulated and 22 downregulated) differentially expressed miRNAs were obtained from comparing the CTEPH group compared with the control group. The heat map of these differentially expressed miRNAs is shown in Figure 1.
3.2. Target Gene of Differentially Expressed miRNA Prediction Analysis
A total of 34386 target gene pairs were obtained from upregulated miRNAs and 16751 from downregulated miRNAs. The top 10 results for the number of target genes regulated by differentially expressed miRNAs are shown in Table 3. Of the miRNAs, miR-3148 regulated the most target genes.
3.3. Functional Enrichment Analysis for Differentially Expressed miRNAs
As shown in Figure 2, the top 5 upregulated miRNAs were mainly enriched in pathways in cancer and axon guidance, and the top 5 downregulated miRNAs were mainly enriched in pathways in cancer and apelin signaling pathway. Among them, miR-3148 was significantly enriched in pathways in cancer and axon guidance.
3.4. Target Genes Coregulated by Differentially Expressed miRNAs Analysis
The coregulated networks for upregulated and downregulated differentially expressed miRNAs were shown in Figure 3. The number of coregulated genes (top 10) was shown in Table 4. It showed that the number of target genes coregulated by miR-3148 and other miRNAs was the most.
3.5. The Network Construction for Target Genes Regulated by Differentially Expressed miRNAs
We constructed the miRNA-Target network for the upregulated and downregulated differentially expressed miRNAs, respectively (Figure 4). ONECUT2 (One Cut Homeobox 2), RC3H1 (Ring Finger and CCCH-Type Domains 1), and SLC1A2 (Solute Carrier Family 1 Member 2) were regulated by 19 upregulated miRNAs; ONECUT2 and RAB6B (Member RAS Oncogene Family) were regulated by 11 downregulated miRNAs.
3.6. Functional Enrichment Analysis of the Target Genes of the Key miRNAs
The target genes regulated by upregulated differentially expressed miRNAs were mainly enriched in 21 GO terms and 16 KEGG pathways, and the target genes regulated by downregulated differentially expressed miRNAs were mainly enriched in 45 GO terms and calcium signaling pathway. Among them, the top 5 results were shown in Table 5. For example, AR (androgen receptor), a target gene of hsa-miR-3148, was enriched in pathways in cancer. PRKCA (Protein Kinase C Alpha), also a target gene of hsa-miR-3148, was enriched in 15 of 16 KEGG pathways, such as pathways in cancer, glioma, and ErbB signaling pathway.
|Term represents the identification number of GO-BP or KEGG pathway. Description represents the name of the GO-BP or KEGG pathway. Counts represent the number of genes enriched in the GO-BP or KEGG pathway.|
3.7. Detection of miRNAs Using RT-PCR
As shown in Figure 5, the expression of hsa-miR-3148 in CTEPH samples was significantly lower than that of the control samples ().
CTEPH is the fourth types of PH, and the roles of miRNAs in several diseases progression such as PH are becoming increasingly evident . In the present study, we carried out microarray analysis and detection of miRNAs to understand the key miRNAs associated with CTEPH. The results showed that miR-3148 regulated the most target genes and was significantly enriched in pathways in cancer, glioma, and ErbB signaling pathway. Furthermore, the number of target genes coregulated by miR-3148 and other miRNAs was the most. AR (androgen receptor), a target gene of hsa-miR-3148, was enriched in pathways in cancer. PRKCA (Protein Kinase C Alpha), also a target gene of hsa-miR-3148, was enriched in 15 of 16 KEGG pathways, such as pathways in cancer, glioma, and ErbB signaling pathway. In addition, the RT-PCR results showed that the expression of hsa-miR-3148 in CTEPH samples was significantly lower than that in control samples ().
It has been reported that miRNA-3148 modulates the differential gene expression of the SLE- (systemic lupus erythematosus-) associated TLR7 (toll-like receptor 7) variant , and TLR7 mediates relaxation of airways through nitric oxide production . In our present study, miR-3148 was demonstrated to be an important miRNA for CEPTH by bioinformatics analysis and RT-PCR. Therefore, although not too much previous studies reported the roles of miRNA-3148 in CEPTH, we inferred that miR-3148 may play important roles in CTEPH according to the present study.
Furthermore, AR, one target gene of hsa-miR-3148, was enriched in pathways involved in cancer. PRKCA, also a target gene of hsa-miR-3148, was enriched in pathways in cancer, glioma, and ErbB signaling pathway. The hsa-miR-3148 was significantly enriched in pathways in cancer, glioma, and ErbB signaling pathway. Previous studies have reported that androgens play a critical role in cardiovascular disease  and are associated with pulmonary arterial hypertension , and AR had been identified in the right and left ventricles . The changes in membrane translocation and protein expression of cPKCα, βI, βII, and nPKCδ are involved in the development of hypoxia-induced rat pulmonary hypertension . An organized thrombus in major pulmonary arteries is typically in association with other diseases, such as lung cancer . There is a very high incidence of symptomatic venous thromboembolisms for patients with glioma . Grant et al. indicated that modulation of ErbB signaling pathway could lead to increased cell apoptosis and loss of clonogenic survival , and cell proliferation was related to pulmonary hypertension [27, 28]. Although no previous studies have suggested direct associations between genes, including AR and PRKCA or pathways in cancer, gliomas, ErbB signaling pathway, and CTEPH, they led to our hypothesis that AR, PRKCA, and pathways in cancer, gliomas, and ErbB signaling pathway are associated with CTEPH. Combined with the results of the present study, we suggest that hsa-miR-3148 may play roles in CTEPH via hsa-miR-3148-AR-pathways in cancer or hsa-miR-3148-PRKCA-pathways in cancer/glioma/ErbB signaling pathway.
In conclusion, we suggest that hsa-miR-3148-AR-pathways in cancer or hsa-miR-3148-PRKCA-pathways in cancer/glioma/ErbB signaling pathway may be the key mechanisms in CTEPH. However, there are limitations in our study, such as the relatively small sample size; hence, further studies are needed.
Highlights. (1) Microarray analysis and detection of significant miRNA were performed. (2) MiR-3148 may play important roles in CTEPH. (3) The pathways in cancer, glioma, and ErbB signaling pathway may be vital for CTEPH.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
This study was supported by the National Natural Science Foundation of China (81300044, 81270117, 81570049, 81200042, 81200041, and 31670928), Beijing Natural Science Foundation (7162069 and 7152062), Beijing Municipal Administration of Hospitals’ Youth Programme (QML20160301), National Key Research and Development Plan of China (2016YFC0905600), and the open project of Beijing Key Laboratory of Respiratory and Pulmonary Circulation Disorders (2014HXFB03).
- C. O’Connell, D. Montani, L. Savale et al., “Chronic thromboembolic pulmonary hypertension,” Presse Medicale, vol. 44, no. 12, pp. e409–e416, 2015.
- N. Galiè, M. Humbert, J. Vachiery et al., “2015 ESC/ERS Guidelines for the diagnosis and treatment of pulmonary hypertension: the Joint task force for the diagnosis and treatment of pulmonary hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT),” European Heart Journal, vol. 37, no. 1, pp. 67–119, 2016.
- I. M. Lang, R. Pesavento, D. Bonderman, and J. X.-J. Yuan, “Risk factors and basic mechanisms of chronic thromboembolic pulmonary hypertension: a current understanding,” European Respiratory Journal, vol. 41, no. 2, pp. 462–468, 2013.
- M. Delcroix, K. Kerr, and P. Fedullo, “Chronic thromboembolic pulmonary hypertension: epidemiology and risk factors,” Annals of the American Thoracic Society, vol. 13, pp. S201–S206, 2016.
- L. Wang, L. Guo, J. Liu et al., “MicroRNA expression profile of pulmonary artery smooth muscle cells and the effect of let-7d in chronic thromboembolic pulmonary hypertension,” Pulmonary Circulation, vol. 3, no. 3, pp. 654–664, 2013.
- L. C. Huber, S. Ulrich, C. Leuenberger et al., “Featured Article: microRNA-125a in pulmonary hypertension: regulator of a proliferative phenotype of endothelial cells,” Experimental Biology and Medicine, vol. 240, no. 12, pp. 1580–1589, 2015.
- A. Courboulin, R. Paulin, N. J. Giguère et al., “Role for miR-204 in human pulmonary arterial hypertension,” Journal of Experimental Medicine, vol. 208, no. 3, pp. 535–548, 2011.
- Z. Chen, T. Nakajima, N. Tanabe et al., “Susceptibility to chronic thromboembolic pulmonary hypertension may be conferred by miR-759 via its targeted interaction with polymorphic fibrinogen alpha gene,” Human Genetics, vol. 128, no. 4, pp. 443–452, 2010.
- B. Wu, “Differential gene expression detection using penalized linear regression models: the improved SAM statistics,” Bioinformatics, vol. 21, no. 8, pp. 1565–1571, 2005.
- H. Dweep, C. Sticht, P. Pandey, and N. Gretz, “MiRWalk—database: prediction of possible miRNA binding sites by ‘walking’ the genes of three genomes,” Journal of Biomedical Informatics, vol. 44, no. 5, pp. 839–847, 2011.
- H. Dweep and N. Gretz, “MiRWalk2.0: a comprehensive atlas of microRNA-target interactions,” Nature Methods, vol. 12, article 697, 2015.
- G. Yu, L.-G. Wang, Y. Han, and Q.-Y. He, “clusterProfiler: an R package for comparing biological themes among gene clusters,” OMICS, vol. 16, no. 5, pp. 284–287, 2012.
- P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003.
- M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000.
- M. Kanehisa and S. Goto, “KEGG: kyoto encyclopedia of genes and genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000.
- D. W. Huang, B. T. Sherman, Q. Tan et al., “The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists,” Genome Biology, vol. 8, no. 9, article R183, 2007.
- A. Zakrzewicz, F. M. Kouri, B. Nejman et al., “The transforming growth factor-β/Smad2,3 signalling axis is impaired in experimental pulmonary hypertension,” European Respiratory Journal, vol. 29, pp. 1094–1104, 2007.
- Y. Deng, J. Zhao, D. Sakurai et al., “MicroRNA-3148 modulates differential gene expression of the SLE-associated TLR7 variant,” Arthritis research & therapy, vol. 14, article A5, 2012.
- M. G. Drake, G. D. Scott, B. J. Proskocil, A. D. Fryer, D. B. Jacoby, and E. H. Kaufman, “Toll-like receptor 7 rapidly relaxes human airways,” American Journal of Respiratory and Critical Care Medicine, vol. 188, no. 6, pp. 664–672, 2013.
- R. K. Dubey, S. Oparil, B. Imthurn, and E. K. Jackson, “Sex hormones and hypertension,” Cardiovascular Research, vol. 53, no. 3, pp. 688–708, 2002.
- K. M. Mair, A. K. Z. Johansen, A. F. Wright, E. Wallace, and M. R. Maclean, “Pulmonary arterial hypertension: basis of sex differences in incidence and treatment response,” British Journal of Pharmacology, vol. 171, no. 3, pp. 567–579, 2014.
- E. Lizotte, S. A. Grandy, A. Tremblay, B. G. Allen, and C. Fiset, “Expression, distribution and regulation of sex steroid hormone receptors in mouse heart,” Cellular Physiology & Biochemistry International Journal of Experimental Cellular Physiology Biochemistry & Pharmacology, vol. 23, article 75, 2009.
- Y. Shi, C. Wang, S. Han et al., “Determination of PKC isoform-specific protein expression in pulmonary arteries of rats with chronic hypoxia-induced pulmonary hypertension,” Medical Science Monitor International Medical Journal of Experimental & Clinical Research, vol. 18, pp. 69–75, 2012.
- W. R. Auger, N. H. Kim, K. M. Kerr, V. J. Test, and P. F. Fedullo, “Chronic thromboembolic pulmonary hypertension,” Clinics in Chest Medicine, vol. 28, no. 1, pp. 255–269, 2007.
- T. J. Semrad, R. O’Donnell, T. Wun et al., “Epidemiology of venous thromboembolism in 9489 patients with malignant glioma,” Journal of Neurosurgery, vol. 106, no. 4, pp. 601–608, 2007.
- S. Grant, L. Qiao, and P. Dent, “Roles of ERBB family receptor tyrosine kinases, and downstream signaling pathways, in the control of cell growth and survival,” Frontiers in Bioscience: A Journal and Virtual Library, vol. 7, pp. d376–d389, 2002.
- X. Yang, L. Long, M. Southwood et al., “Dysfunctional Smad signaling contributes to abnormal smooth muscle cell proliferation in familial pulmonary arterial hypertension,” Circulation Research, vol. 96, no. 10, pp. 1053–1063, 2005.
- F. Perros, P. Dorfmüller, R. Souza et al., “Fractalkine-induced smooth muscle cell proliferation in pulmonary hypertension,” European Respiratory Journal, vol. 29, no. 5, pp. 937–943, 2007.
Copyright © 2017 Ran Miao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.