MicroRNA and mRNA analysis of angiotensin II‑induced renal artery endothelial cell dysfunction
- Authors:
- Published online on: March 19, 2020 https://doi.org/10.3892/etm.2020.8613
- Pages: 3723-3737
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Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Recent studies have found that the local renin-angiotensin system (RAS) might contribute to glomerulosclerosis and renal interstitial fibrosis (1). The organ-specific roles exerted by angiotensin II (Ang II), which is the most potent biologically active product of the RAS, have also been studied (2). The RAS is overactivated in vivo once hypertension occurs. Overproduced Ang II directly constricts vascular endothelial cells (ECs), causes changes in diastolic and contractile substances, increases the synthesis and release of endothelium-derived vasoconstrictors such as endothelin-1 (ET-1) and thromboxane A2, reduces the production of endothelium-derived vasodilators such as nitric oxide (NO) and ultimately results in vascular endothelial damage and retention of sodium and water (3,4). Further studies demonstrated that inappropriate activation of intrarenal Ang II plays a central role in the pathogenesis of hypertension and renal injury (5). The role of renal artery ECs in self-regulation is associated with cell autophagy and energy homeostasis. However, activation of the endothelium by elevated blood pressure is followed by endothelial dysfunction, which eventually leads to endothelial disintegration (6). In this context, dysfunctional ECs may continue generating ATP by glycolysis for a long period of time and keep their mitochondrial membrane potential in a depolarized state that can be reverted. Furthermore, under these circumstances, the autophagy pathway may be activated to maintain glycolytic-dependent ATP production (7). The effect of hypertension on renal vascular endothelium is directly related to hypertensive nephropathy (HN), but few studies have harnessed the power of transcriptome sequencing or microarray analysis to identify the potential vulnerabilities of hypertensive renal artery injury. Therefore, the molecular mechanism of Ang II on renal artery ECs has important research value and significance.
MicroRNAs (miRs/miRNAs) are post-transcriptional regulators of gene expression. These small (20-25 nucleotides long) noncoding RNAs bind to a target recognition site (seed sequence) in the 3'-untranslated regions of mRNA transcripts, leading to mRNA degradation and/or inhibition or activation of protein translation, depending on the complementarity of the miRNA with the target Mrna (8). A growing number of miRNAs, including miRNA-let-7b, miRNA-431 and miRNA-29 (9-11), are implicated in the promotion or suppression in the initiation and progression of hypertension. Ang II-mediated STAT3 activation in kidney epithelial cells results in hypertensive kidney disease (12); however, the detailed mechanisms and regulatory role as therapeutic targets of miRNAs underlying renal artery EC injury induced by Ang II remain poorly understood. Hence, the present study focused on the miRNAs involved in renal artery EC dysfunction.
The present study constructed an Ang II-induced rat renal artery EC (RRAEC) injury model. The mitochondrial membrane potential and glycolysis levels were determined to assess mitochondrial function and cellular energy supply of RRAECs exposed to Ang II. The stability of the intracellular environment was evaluated by measuring the level of autophagy. Subsequently, mRNA and miRNA sequencing and integrated analysis of differentially expressed (DE) miRNAs and their target mRNAs were performed to further explore the precise molecular mechanisms responsible for Ang II-induced renal artery endothelial dysfunction. The results of the present study could provide new insights into the potential therapeutic targets of HN.
Materials and methods
Cell culture and reagents
RRAECs (cat. no. RAEC; Sixin) were cultured in DMEM (cat. no. 10-013-CVR; Corning, Inc.) supplemented with 10% FBS (cat. no. 10099-141; Gibco; Thermo Fisher Scientific, Inc.) and 1% penicillin/streptomycin (cat. no. SV30010; HyClone; GE Healthcare Life Sciences) at 37˚C and 5% CO2. Cells from passages 2-8 were used for subsequent experiments. RRAECs were seeded in six-well plates at a density of 2x104 cells/well for 24 h with exposure to Ang II (5x10-7 mol/l; cat. no. A9290; Beijing Solarbio Science & Technology Co., Ltd.) at 37˚C, while untreated RRAECs were used as a negative control. The cells were serum-starved overnight before further treatment. All samples were assayed in triplicate.
Detection of mitochondrial membrane potential
JC-1 is a mitochondrion-selective dye widely used to detect mitochondrial membrane potential (ΔΨm), which reversibly changes color from green to red as the membrane potential increases (values of >80-100 mv). The degree of mitochondrial depolarization is measured by the relative proportion of red and green fluorescence (13). Following 24 h of Ang II exposure as described above, the cells were collected and washed with PBS (cat. no. 21-040-CVR; Corning, Inc.). Washed cells were resuspended in JC-1 staining solution (cat. no. M8650; Beijing Solarbio Science & Technology Co., Ltd.) and incubated at 37˚C for 20 min. RRAECs were then washed twice with the diluted working solution (JC-1 staining solution; 1X). Mitochondrial membrane potential analysis was performed using a flow cytometer (BD Biosciences) and analyzed by CytExpert for DXFLEX 2.0 (Beckman Coulter, Inc.).
Seahorse XF analysis
The Seahorse XF Glycolysis Stress Test performed using the Seahorse XF(e)24 Cell Energy Metabolic Analyzer (Agilent Technologies, Inc.) directly measures the extracellular acidification rate (ECAR) of living cells in real time, reflecting glycolytic function in cells. Cell density was optimized by inoculating different amounts of RRAECs (1-8x104 cells/well) into a 24-well Seahorse XF Cell Culture Microplate (cat. no. 100777-004; Agilent Technologies, Inc.) at 37˚C and 5% CO2. Subsequent assays were performed using 3x104 cells/well. A sensor cartridge was hydrated in a Seahorse XF Calibrant (Agilent Technologies, Inc.) at 37˚C in a non-CO2 incubator overnight. XF Glycolysis Stress Test kit medium (cat. no. 103020-100; Agilent Technologies, Inc.) was heated to 37˚C, and the pH was adjusted to 7.4±0.1. The cell culture microplate was removed from the 37˚C CO2 incubator, and the RRAECs were washed with XF glycolysis assay medium and then placed in a 37˚C, non-CO2 incubator for 1 h prior to the assay. Injection ports A, B, and C were loaded with glucose (10 mmol/l), oligomycin (1 µmol/l) and 2-deoxy-glucose (50 mmol/l; included in the Glycolysis Stress assay kit), according to the manufacturer's protocol. The experimental template was set up or imported on the XF controller. The default Mix-Wait-Measure time was 3 min-2 min-3 min. Seahorse Wave software (version 2.2.0; Agilent Technologies, Inc.) was used for data calculation. The XF Glycolysis Stress Test was used to measure glycolysis as the ECAR that is reached by RRAECs after the addition of saturating amounts of glucose. The glycolytic reserve was defined as the difference between glycolytic capacity and glycolysis rate. Prior to glucose injection, ECAR was referred to as non-glycolytic acidification and was caused by processes in the cell other than glycolysis.
Autophagy rate assay
The effect of Ang II on autophagy rate was assessed with a dansylcadaverine (MDC) Autophagy Assay kit (cat. no. MAK138-1KT; Sigma-Aldrich; Merck KGaA) by flow cytometry. MDC is an acidic vesicle-specific fluorescent marker that directly reflects the number of acidic vesicles in the cells (14). RRAECs were treated with Ang II for 24 h and then trypsinized and collected. Cells (5x105) were washed with PBS twice, incubated with MDC staining solution at 37˚C for 1 h and then incubated with 4% paraformaldehyde at room temperature for 15 min. The stained cells were detected with a FACScan flow cytometer at a wavelength of 488 nm, and the total autophagy rate (without distinguishing between macroautophagy and mitochondrial autophagy) was statistically analyzed. Data were analyzed using cell Quest 3.1 software (Becton, Dickinson and Company).
Observation of autophagosomes by transmission electron microscopy (TEM)
As the most reliable method and gold standard for detecting phagocytosis (15), TEM was used to clearly observe the independent bilayer membrane structure of autophagosomes and various autophagic morphological changes at x10,000 magnification. Autophagosomes in the control group and the Ang II group were observed by TEM (JEOL, Ltd.). RRAECs at a density of 1x105 cells/well were seeded in six-well plates with or without exposure to Ang II for 24 h. Subsequently, the cells were gently scraped and centrifuged at a rate of 2,000 x g for 10 min at 37˚C in PBS, following which RRAECs were fixed with 2.5% glutaraldehyde containing 0.1 mol/l sodium cacodylate. Samples were fixed using 1% osmium tetroxide, followed by dehydration with an increasing concentration gradient of acetone solution. Samples were then embedded in epoxy resin provided by a SPI-PonTM 812 Epoxy Resin Embedding kit (cat. no. 02635-AB; SPI Supplies). The resin was subsequently polymerized at 37˚C for 6 h, 45˚C for 12 h and 67˚C for 24 h. All cell samples were then cut into 50-nm sections and stained with 3% uranyl acetate for 10 min and lead citrate for 5 min at room temperature. Images were acquired using a TEM. Sample processing and image capture were performed by Weiya Biotechnology Co., Ltd.
RNA extraction and quality control
Total RNA was isolated using the miRNeasy Mini kit (Qiagen GmbH), following which RNA concentration and quality were determined using a Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, Inc.) and a NanoDrop™ One spectrophotometer (Thermo Fisher Scientific, Inc.). The integrity of total RNA was assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies Inc.), and samples with RNA integrity number values >7.0 were used for sequencing.
Library construction for RNA-sequencing (RNA-seq) and sequencing procedures
Strand-specific libraries were prepared using the TruSeq® Stranded Total RNA Sample Preparation kit (Illumina, Inc.) following the manufacturer's instructions. Briefly, ribosomal RNA (rRNA) was removed from total RNA using Ribo-Zero rRNA removal beads. Following purification, the mRNA was fragmented into small pieces using divalent cations at 94˚C for 8 min. The cleaved RNA fragments were reverse transcribed into first strand cDNA using reverse transcriptase and random primers. This was followed by second strand cDNA synthesis using DNA Polymerase I and RNase H. The cDNA fragments then went through an end repair process, the addition of a single ‘A’ base and adapter ligation. The products were then purified and amplified by PCR to create the final cDNA library. Purified libraries were quantified by a Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, Inc.) and validated with an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.) to confirm the insert size and to calculate the mole concentration. Clusters were generated by cBot, (Illumina, Inc.) with the library diluted to 10 pm and sequenced on a HiSeq X Ten sequencer (Illumina, Inc.). Library construction and sequencing were performed at Shanghai Sinomics Corporation.
Library construction for miRNA-sequencing (miRNA-seq) and sequencing procedures
Paired-end libraries were synthesized using the QIAseq miRNA Library kit (Qiagen GmbH) following the TruSeq™ RNA Sample Preparation Guide (QIAseq miRNA Library). The products were then purified and enriched with PCR to create the final cDNA library. Purified libraries were quantified by a Qubit® 2.0 Fluorometer (Thermo Fisher Scientific, Inc.) and validated with an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc.) to confirm the insert size and calculate the molar concentration. Clusters were generated by cBot (Illumina, Inc.) with the library diluted to 10 pm and then sequenced on the Illumina HiSeq X™ Ten sequencer (Illumina, Inc.). Library construction and sequencing were performed at Sinotech Genomics Co., Ltd.).
Data analysis for gene expression
RNA-seq raw reads were pre-processed by filtering out rRNA reads, sequencing adapters, short-fragment reads and other low-quality reads. Tophat version 2.0.9 (https://ccb.jhu.edu/software/tophat/index.shtml) was used to map the clean reads to the Rnor_6.0.91 reference genome [Rnor_6.0 (GCA_000001895.4)] using alignment with two mismatches. Following genome mapping, Cufflinks version 2.1.1 (http://cole-trapnell-lab.github.io/cufflinks/) was run with a reference annotation to generate fragments per kilobase of exon model per million reads mapped (FPKM) values for known gene models. Differentially expressed genes were identified using edgeR package. The P-value significance threshold in multiple tests was set by the false discovery rate. The fold changes (FCs) were also estimated according to the FPKM of each sample. The differentially expressed genes were selected using the following filtering criteria: P<0.05 and FC>1.5 or <0.67. Different samples and genes were clustered and classified and a heatmap was generated to display the expression of genes in different samples using MeV_4_6_0 software (multiple experiment viewer). Gene Ontology (GO) (http://geneontology.org/) adapted Fisher test, cluster profiler packages in R-3.4.3 and Bioconductor 3.6 (16,17) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) (https://www.kegg.jp/) pathway analysis were used to analyze the functions of the genes in the pathways. Reads of each sample containing miRNA sequences were compared with existing sequences in a miRNA database (miRBase; http://www.mirbase.org/) and the predicted results of new miRNAs generated by MiRDeep2 version 2.0.0.5 software to calculate the miRNA expression level (count number). miRNA expression was screened by counts per million. DE sequencing software (DESeq 1.30.0; https://bioconductor.org/packages/DESeq/) was used to analyze the expression between samples and identify DE miRNAs with P<0.05 and FC>1.5 or <0.67.
Reverse transcription-quantitative PCR (RT-qPCR) analysis of miRNA and mRNA expression
To verify the sequencing results, 15 DE genes closely related to endothelial dysfunction were selected for RT-qPCR analysis: Nine miRNAs (miR-200a-3p, miR-200b-3p, miR-200c-3p, miR-192-5p, miR-223-3p, miR-194-5p, miR-494-3p, miR-429 and miR-29b-3p) and six mRNAs [tumor protein D52, XK related 8 (Xkr8), ETS transcription factor ELK4 (Elk4), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (Pfkfb3), selenoprotein P, tumor protein D52 and zinc finger E-box binding homeobox 2].
miRNAs were extracted using the MiPure Cell/Tissue miRNA kit (cat. no. RC201; Vazyme Biotech Co., Ltd.). Purified miRNAs were then reverse transcribed into cDNA using a Mir-X miRNA First-Strand Synthesis kit (cat. no. 638313; Takara Biotechnology Co., Ltd.) according to the manufacturer's protocol and subjected to qPCR using the TB Green Ex Taq II kit (cat. no. RR820A; Takara Biotechnology Co., Ltd.). The thermocycling conditions were as follows: Denaturation at 95˚C for 10 sec; followed by 40 cycles at 95˚C for 5 sec and 60˚C for 20 sec. The dissociation curve was set at: 95˚C for 60 sec, 55˚C for 30 sec and 95˚C for 30 sec. Reactions were performed on a LightCycler 480 Instrument II (Roche Diagnostics GmbH). All reactions were run in triplicate, and data were normalized to the expression levels of U6. Relative quantification of miRNA expression was calculated using the 2-ΔΔCq method (18).
Total RNA was isolated from cultured cells using RNAiso Plus (cat. no. 9108; Takara Bio, Inc.). A total of 2 µg RNA was treated with gDNA Eraser and reverse transcribed into cDNA using the PrimeScript RT Reagent kit (cat. no. RR047A; Takara Biotechnology Co., Ltd.) according to the manufacturer's protocol. Gene-specific primers were designed and purchased from Sangon Biotech, Co., Ltd. qPCR was performed as described above. Data were analyzed using the 2-ΔΔCq method with GAPDH as an internal reference gene. All samples were analyzed in triplicate. The primer sequences of the corresponding miRNAs and mRNAs are listed in Table I.
Target gene prediction of DE miRNAs
The miRanda algorithm (19) was used to predict miRNA target genes. The miRanda algorithm comprehensively predicts miRNA target genes based on miRNA-mRNA sequencing matching or energy stability and uses a dynamic programming algorithm to search for regions where miRNAs have complementary sequences with target mRNAs and form stable double-strands.
Construction of a network map of miRNAs and target genes
A global network of DE miRNAs and target genes was constructed using Cytoscape 3.7.2 software (https://cytoscape.org/).
Statistical analysis
Data were analyzed with SPSS 17.0 software (SPSS, Inc.) and presented as the mean ± SD from at least three separate experiments. Two-tailed independent sample t-test was used to compare the mean values between two groups. P<0.05 was considered to indicate a statistically significant difference.
Results
Ang II decreases the mitochondrial membrane potential of RRAECs
The F value, defined as the proportion of cells with decreased mitochondrial membrane potential, of cells in the Ang II group was significantly higher compared with the control group (P<0.001; Fig. 1A-C). The F value in the Ang II group was 17.99±1.23% while the F value in the control group was 4.64±0.88%. The results suggested that the normal mitochondrial aerobic respiration of RRAECs was partially inhibited in response to mitochondrial dysfunction.
RRAECs treated with Ang II are highly glycolytic
The Seahorse XF Glycolysis Stress Test provides a standard and comprehensive method to assess the key parameters of glycolytic flux, including glycolysis, glycolytic capacity and glycolytic reserve, by directly measuring the extracellular acidification rate (Fig. 1D and E). The glycolysis value (control group, 8.25±8.12 mpH/min; Ang II group, 22.57±3.76 mpH/min; Fig. 1F), glycolytic capacity value (control group, 18.31±12.01 mpH/mi; Ang II group, 62.28±5.03 mpH/min; Fig. 1G) and glycolytic reserve value (control group, 10.06±4.25 mpH/min; Ang II group, 39.71±3.44 mpH/min; Fig. 1H) of RRAECs in the Ang II group were significantly increased compared with the control group. Taken together, these findings suggested that Ang II may cause glycolysis pathway activation.
Ang II promotes autophagy activation in RRAECs
The autophagy rate of RRAECs increased by ~4-fold (15.12±1.45) after 24 h of Ang II treatment compared with the autophagy rate of untreated RRAECs (3.87±1.03; Fig. 2A-C).
Autophagosomes were evaluated in RRAECs cultured with or without Ang II treatment. RRAECs in the control group had a smooth nuclear membrane, a uniform distribution of nuclear chromatin and abundant organelles in the cytoplasm (Fig. 2D). By contrast, TEM showed an increase in the formation of autophagic vesicles in RRAECs treated with Ang II (Fig. 2E). Mitochondria were swollen and vacuolized in the cytoplasm of Ang II-treated cells (Fig. 2E). The number of autophagic corpuscles after 24 h of Ang II exposure significantly increased compared with the control group (Fig. 2F). Collectively, the data indicated that Ang II might activate the autophagy pathway in RRAECs.
Differential expression profiling of mRNAs and miRNAs
A total of 98,134,568-132,788,030 raw mRNA reads with clean ratios of 98.315-98.503% were collected for libraries of RRAECs in the Ang II and control groups. After filtering out low-quality raw reads and trimming the 3' and 5' adapter sequences, 96,665,811-130,551,102 clean reads were used for genomic alignment (Table II).
A total of 30,446,150-35,655,373 raw miRNA reads with clean ratios of 47-54% were collected for libraries of RRAECs in the Ang II and control groups. After filtering out low-quality raw reads, 15,852,783-18,500,681 clean reads were used for genomic alignment (Table III).
DE mRNAs are shown in Fig. 3A and C. After 24 h of Ang II exposure, a total of 443 DE mRNAs were identified (66 upregulated and 377 downregulated; FC>1.5 or <0.67; P<0.05). The mRNA with the most significant difference was 5'-nucleotidase cytosolic IB (FC=10.46876; P=0.007145). DE miRNAs are shown in Fig. 3B and D. After 24 h of Ang II treatment, a total of 58 statistically significant DE miRNAs were detected (55 upregulated and 3 downregulated; FC>1.5 or <0.67; P<0.05). The miRNA with the most significant difference was miRNA-7b (FC=12.44444; P=5.46x10-8). The 17 upregulated and 3 downregulated miRNAs are presented in Table IV.
 | Table IVTop 10 upregulated and downregulated miRNAs and mRNAs in the Ang II group compared with the control group. |
RT-qPCR validation of significantly DE miRNAs and DE mRNAs
RT-qPCR was performed to validate the RNA-seq data. All nine detected miRNAs were upregulated in the Ang II group compared with the control group (Fig. 4A). The expression results of six mRNAs were consistent with the results of the RNA-seq, with the exception of Elk4 and Xkr8 (Fig. 4B). These data corroborated the results of the miRNA sequencing analysis, indicating that the RNA-seq data was reliable.
Functional analysis of DE mRNAs
GO analysis describes the properties of genes and gene products and provides functional annotation (20). GO covers three aspects of biology: Cellular component (CC), molecular function (MF), and biological process (BP). DE genes were sorted based on the associated P-values, and the top 30 terms are listed in Fig. 5A. After 24 h of Ang II treatment, the most enriched GO terms associated with endothelial dysfunction in each classification were ‘urogenital system development’ (GO ID, GO: 0001655; type, BP; P=7.49x10-8), ‘apical part of cell’ (GO ID, GO: 0045177; type, CC; P=5.66x10-14) and ‘oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor’ (GO ID, GO: 0016616; type, MF; P=3.19x10-5). Various enriched biological processes were also observed in the present study, including ‘glycolytic process’ (Pfkfb3, glycerol-3-phosphate dehydrogenase 1, fructose-bisphosphatase 1 and peroxisome proliferative activated receptor, gamma, coactivator 1 alpha), ‘regulation of nitric oxide biosynthetic process’ (aspartate dehydrogenase domain containing, flavin containing dimethylaniline monoxygenase 1 and 3, and hydroxysteroid 11-b dehydrogenase 1), ‘regulation of systemic arterial blood pressure mediated by a chemical’ [N-deacetylase and N-sulfotransferase 2, serpin family F member 2, Hydroxysteroid dehydrogenase11b2 (Hsd11b2) and glutamyl aminopeptidase] (Fig. 5A). The enriched pathways for DE mRNAs were analyzed by KEGG annotation, and the 20 most significantly enriched pathways are listed in Fig. 5B. The main enriched pathways of DE mRNAs after 24 h of Ang II exposure were ‘metabolic pathways’ (62 genes), ‘glycolysis/gluconeogenesis’ (8 genes), ‘PPAR signaling pathway’ (8 genes), ‘fatty acid degradation’ (5 genes), ‘aldosterone-regulated sodium reabsorption’ (4 genes), ‘pyruvate metabolism’ (4 genes), ‘AMPK signaling pathway’ (7 genes) and ‘renin-angiotensin system’ (3 genes). The DE mRNAs and DE miRNAs associated with glycolysis are presented in the Tables SI and SII (21-31).
Prediction and functional analysis of DE miRNA targets
miRNA target prediction was performed to explore the potential regulatory roles of miRNAs in RRAECs. Bioinformatics analysis suggested the presence of 4,294 target genes of DE miRNAs after treatment with Ang II for 24 h. As shown in Fig. 5C, the most enriched GO terms of DE miRNA target genes associated with endothelial dysfunction in each class were ‘regulation of protein serine/threonine kinase activity’ (GO ID, GO: 0071900; type, BP; P=0.000393044), ‘vacuolar membrane’ (GO ID, GO: 0005774; type, CC; P=3.34x10-6) and ‘transcriptional activator activity, RNA polymerase II transcription regulatory region sequence-specific binding’ (GO ID, GO: 0001228; type, MF; P=2.40x10-6). KEGG analysis suggested that the pathways of the DE miRNA target genes were mainly enriched in the ‘mTOR signaling pathway’, ‘AMPK signaling pathway’, ‘autophagy-animal’, ‘Proximal tube bicarbonate reclamation’, ‘MAPK signaling pathway’, ‘insulin resistance’ and ‘Ras signaling pathway’ (Fig. 5D). Fig. 6, which was generated using Pathview package in R, presented DE genes in the mTOR signaling pathway. DE mRNAs and DE miRNAs associated with autophagy are presented in Tables SIII and SIV (32-48).
Network map of DE miRNAs and their target genes
A global network of miRNAs and target genes was constructed to determine the functional interactions between the two types of genes (Fig. 7A). Furthermore, to evaluate the contribution of specific miRNAs, the current study focused on the miRNA-200 family, which was highly and specifically expressed in RRAECs cultured with Ang II and may be a potent regulator of renal artery EC dysfunction. Therefore, a local network map of the miRNA-200 family and their corresponding target genes was constructed (Fig. 7B).
Discussion
Targeting unique miRNAs which are highly expressed in ECs may be a promising approach in the development of therapeutic tools for diseases associated with endothelial dysfunction. The present study proposed that Ang II activated autophagy pathway in RRAECs, as well as glycolysis pathways to compensate for the lack of the total cellular ATP level caused by mitochondrial respiratory disorder (49). To test this hypothesis, the differential expression of miRNAs and mRNAs in RRAECs after Ang II exposure was profiled in order to extend the list of potential candidate genes involved in hypertensive renal disease. The top 10 DE mRNAs identified in RNA-seq, including matrix metalloproteinase (MMP)-28, anoctamin-1 (ANO1), and myosin light chain kinase 2 (Mylk2), were closely related to EC dysfunction. MMP-28, the newest member of the MMP family, affects the function of hypertensive target organs by impairing the microvascular endothelium (50). MMP-28 overexpression causes EC apoptosis and capillary degeneration, leading to renal arteriosclerosis by directly cleaving the extracellular domain of vascular endothelial growth factor receptor 2 and also increases the expression and activity of MMP-2, causing damage to renal function (51). MMP-28, which is associated with early hypertensive renal disease, is one of the major risk factors for microalbuminuria in hypertensive patients (52). ANO1 is a newly discovered calcium-activated chloride channel that regulates EC function and participates in vasoconstriction (53). ANO1 overexpression induces an increase in the opening of voltage-dependent calcium channels on the plasma membrane, causing a large amount of Ca2+ influx, whereas overload of intracellular calcium triggers arterial vasoconstriction and participates in the process of elevated blood pressure in vivo (54). A previous study showed that ANO1 was present at high levels in various vascularities of spontaneously hypertensive rats (55). Decreased barrier function caused by EC damage is the starting point of vascular disease, and Mylk2, which encodes myosin light chain kinase (MLCK), increases myosin activity, increases myoglobin-myosin cross-linking, intensifies cell contraction, widens the cell gap through complex signal transduction mechanisms and ultimately increases endothelial permeability (56). Multiple signaling molecules activate MLCK through different pathways to induce endothelial barrier dysfunction (57). The GO terms associated with hypertensive renal disease caused by endothelial dysfunction in the Ang II group included ‘glycolytic process’ (Pfkfb3, glycerol-3-phosphate dehydrogenase 1, fructose-bisphosphatase 1 and peroxisome proliferative activated receptor, gamma, coactivator 1 alpha), ‘regulation of nitric oxide biosynthetic process’ (aspartate dehydrogenase domain containing, flavin containing dimethylaniline monoxygenase 1 and 3, and hydroxysteroid 11-b dehydrogenase 1) and ‘regulation of systemic arterial blood pressure mediated by a chemical’ [N-deacetylase and N-sulfotransferase 2, serpin family F member 2, Hydroxysteroid dehydrogenase11b2 (Hsd11b2) and glutamyl aminopeptidase]. Numerous reports claimed that reduced bioavailability of NO, as a result of reduced synthesis by ECs, is a key event underlying vascular endothelial damage (58). Hsd11b2, which plays an important regulatory role in the aforementioned biological processes and was significantly downregulated in the Ang II group in the current study, is a newly discovered candidate gene for hypertension that regulates water-salt metabolism and is directly related to blood pressure regulation and the initiation of hypertension (59,60). The KEGG terms enriched by DE mRNAs in the Ang II and control groups were mainly ‘glycolysis/gluconeogenesis’, ‘fatty acid degradation’, ‘pyruvate metabolism’ and the ‘renin-angiotensin system’. Experimental studies showed that glutamine and fatty acids were essential substrates, suggesting a critical role for oxidative phosphorylation and mitochondrial respiration in cells (61); however, ECs were reported to rely on glycolysis to a certain degree, especially when intracellular mitochondrial function was abnormal (62). De Bock et al (63) showed that glycolysis levels in ECs were largely comparable to those in tumor cells but higher compared with other healthy cells. In human umbilical vein ECs, the glycolytic flux was >200-fold higher (~1.5 µmol glucose/h/mg protein) compared with glucose oxidation, fatty acid oxidation, and glutamine oxidation, partly due to the smaller volume of mitochondria in ECs. The high glycolysis rate maintains lactic acid production and plays a pivotal role in promoting angiogenic signaling. ATP produced by glycolysis is a driver of EC rearrangement in neovascularization (64). The present results demonstrated that Ang II caused mitochondrial dysfunction and significantly reduced the mitochondrial membrane potential of RRAECs. Therefore, it may be concluded that ECs tend to rely on glycolysis metabolism to compensate for mitochondrial dysfunction. In addition, fatty acid degradation in ECs is directly related to angiogenesis, which regulates biomass synthesis to a certain extent, especially the production of deoxyribonucleotides required for DNA synthesis during EC proliferation (65). Ang II has direct effects on renal vascular ECs, causing vasoconstriction of both afferent and efferent arterioles, which results in the development of both glomerular capillary hypertension and reduced glomerular filtration rates. Ang II has been shown to stimulate the adrenal cortex to secrete aldosterone, leading to increased reabsorption of sodium and water into the distal renal tubules. In addition, Ang II induces vascular NADPH oxidase and ET-1 expression in the kidneys (66,67). Therefore, the relevance of RAS components in the determination of hypertensive nephropathy demonstrated therapeutic implications.
miRNAs are known to play a role in the occurrence and development of hypertensive renal damage. RNA-seq analysis and RT-qPCR results showed that miR-200a-3p, miR-200b-3p, miR-200c-3p and miR-429 were highly expressed in the Ang II group compared with the normal group. These miRNAs are members of the miRNA-200 family, which regulates the expression of transcriptional factors through temporal and spatial patterns during the development of the anterior kidney (68). Therefore, an in-depth analysis of miRNA-200 family members can be performed in a subsequent study based on the constructed network map of the above four miRNAs and their respective target mRNAs. Recent reports demonstrated that miRNA-200c not only promotes epithelial mesenchymal transdifferentiation by inhibiting zinc finger E-box binding to homologous cassette (ZEB) 2(69), but also upregulates cyclooxygenase 2 expression via ZEB1 to mediate human artery vascular dysfunction (70). The present findings demonstrated that the expression of miRNA-200c was significantly upregulated in the Ang II group compared with the normal group (FC=4.228571; P=4.25x10-8). Further insights into the role of miRNA-200 in ECs will improve the understanding of the molecular mechanisms of Ang II-induced renal artery endothelial dysfunction. A previous study has indicated that exogenous platelet miRNA-223 decreases the expression of insulin-like growth factor 1 receptor in HUVEC and thus promotes advanced glycation end product-induced vascular endothelial cell apoptosis (71). Upregulated expression of miRNA-223 in the Ang II group may provide a promising and novel approach to the treatment of hypertension (72). miRNA-494, which increases lipopolysaccharide-induced apoptosis of human proximal tubular epithelial cells by negatively regulating the cyclic AMP-dependent transcription factor ATF-3 gene, was found to mediate the apoptosis of various cells (73). Whether overexpressed miRNA-494 in the Ang II group (FC=1.550713; P=0.002353) exerts a similar effect requires further investigation.
As small noncoding RNAs, miRNAs exert their regulatory functions by mRNA degradation or translational inhibition (74). In the current study, the target genes of DE miRNAs were mainly enriched in the ‘mTOR signaling pathway’, ‘AMPK signaling pathway’ and ‘autophagy-animal’. Among these, the mTOR signaling pathway, which regulates the growth and differentiation of cells by sensing the stimulation of growth factors and nutrition, is the main nutrient sensor in the cells and serves a critical role in coordinating intracellular energy metabolism and overall energy levels in vivo (75,76). In addition, mTOR is also a key regulator of autophagy initiation, and mTOR activation inhibits the formation of autophagosomes and negatively regulates autophagy (77,78). Autophagy, which can be regulated by the mTOR pathway, is a biological process in which parts of proteins or organelles encapsulated in a cell membrane are transferred to lysosomes for digestion and degradation to maintain cell homeostasis (79). Furthermore, glycolysis flux, apart from mitochondrial respiratory function, has been shown to be a potential treatment for ischemic kidney damage (80). In ECs, glucose is shuttled through hexokinase into the glycolytic pathway. The product of glycolysis is used not only for the synthesis of glycogen, but also for energy production. A key enzyme for glycolysis, 6-phosphate fructose-2-kinase/fructose-2,6-bisphosphatase 3 is regulated by cellular energy status (the AMPK (81) or growth factor AKT (82) signaling pathways) and participates in protein synthesis regulated by mTOR. The present sequencing results also demonstrated that glycolysis was one of the most important energy metabolism pathways of Ang II-induced renal artery EC dysfunction. Ang II may activate the glycolysis and autophagy pathway in RRAECs through several DE mRNAs or the binding of DE miRNA to its target mRNA. Furthermore, several studies revealed that autophagy may regulate cell glycolysis by selectively degrading hexokinase 2(83); glycolysis in turn plays an essential role in autophagy by limiting superoxide levels and maintaining expression of autophagy genes required for autophagic vesicle maturation (84). Therefore, the effect of the miRNA-mediated mTOR signaling pathway on glycolytic metabolism and autophagy is indicative of the pathological mechanism of Ang II-induced renal artery EC dysfunction.
In conclusion, miRNAs that regulate multiple target genes provide an effective link between regulator target genes and complex biological processes that can be identified through miRNA-mRNA interactions. Further studies investigating the miRNAs and their associated pathway will likely result in the development of novel approaches for the treatment of hypertensive kidney damage.
Supplementary Material
Differentially expressed mRNAs related to glycolysis in angiotensin-induced rat renal artery endothelial cells compared with controls.
Differentially expressed miRNAs that have predicted or published target mRNAs related to glycolysis in angiotensin II-induced rat renal artery endothelial cells compared with controls.
Differentially expressed mRNAs related to autophagy in angiotensin II-induced rat renal artery endothelial cells compared with controls.
Differentially expressed miRNAs that have predicted or published target mRNAs related to autophagy in angiotensin II-induced rat renal artery endothelial cells compared with controls.
Acknowledgements
The authors would like to thank Mr. Yixin Yin of the Shanghai Biotechnology Corporation for technical assistance and constructive suggestions.
Funding
This study was supported by the National Natural Science Foundation of China (grant no. 81673812).
Availability of data and materials
The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.
Authors' contributions
YL performed the experiments and drafted the manuscript. YJ designed the study and performed the experiments. WL designed the study, contributed to the discussion and performed important revisions of the article. CH and ZQ contributed to data acquisition and software applications. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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