Skip to main content
NCBI home page
Toxicology Research logoLink to Toxicology Research
. 2016 Sep 9;5(6):1548–1560. doi: 10.1039/c6tx00257a

MicroRNA-338-5p modulates pulmonary hypertension-like injuries caused by SO2, NO2 and PM2.5 co-exposure through targeting the HIF-1α/Fhl-1 pathway†

Xiaotong Ji a, Yingying Zhang a, Tingting Ku a, Yang Yun a, Guangke Li a, Nan Sang a,✉
PMCID: PMC6062392  PMID: 30090456

graphic file with name c6tx00257a-ga.jpgThe role of ambient air pollution is considered to be important in the development of chronic obstructive pulmonary disease (COPD), and pulmonary hypertension (PH) is a common clinical manifestation of COPD.

Abstract

The role of ambient air pollution is considered to be important in the development of chronic obstructive pulmonary disease (COPD), and pulmonary hypertension (PH) is a common clinical manifestation of COPD. However, many studies have mainly focused on the adverse health effects of a single air pollutant, ignoring the combined toxicity of multiple pollutants. In the present study, we co-exposed mice to coal-burning air pollutants (SO2, NO2 and PM2.5), and confirmed PH-like injury occurrence by airflow limitation, marked abnormal endothelin-1 (ET-1) and endothelial nitric oxide synthase (eNOS) expression, and histopathological and ultrastructural alteration. Global microRNA (miRNA) arrays identified three significantly changed miRNAs homologous with humans (miR-338-5p, miR-450b-3p and miR-142-5p), and we targeted miR-338-5p based on real-time reverse transcription-PCR (RT-PCR) validation. Furthermore, bioinformatic and dual-luciferase reporter gene analyses indicated that miR-338-5p bound to 3′-UTR of hypoxia-inducible factor 1α (HIF-1α) mRNA and down-regulation of miR-338-5p led to the increased expression of HIF-1α and its related gene four-and-a-half LIM (Lin-11, Isl-1 and Mec-3) domain 1 (Fhl-1) and contributed to PH. This study provides evidence for the role of miRNAs in PH through targeting HIF-1α/Fhl-1 pathway after air pollutants co-exposure and implies new insights into the molecular markers for COPD caused by air pollution.

1. Introduction

China is the largest producer and consumer of coal in the world; it is the largest user of coal-derived electricity and over 70% of its electricity was from coal in 2014.1 Although coal combustion in large cities was restricted during past periods, the use of coal remained widely without effective control technologies in rural areas, including consumption in extensive civilian stoves for cooking and heating, which was worse in the winter.2 The combination of population growth and urbanization and industrialization makes this situation worse in cities for energy production and chemical and metallurgical industries. Particulate matter (PM), sulfur dioxide (SO2) and nitrogen oxides (NOX) are the major air pollutants in coal-burning air pollution. Based on the data of the Environmental Aspect Bulletin in Shanxi province, a typically coal-burning ambient pollution location in China, issued on 2011–2012, the pollution load for respirable PM accounted for 42.8%, followed by SO2 and NOX, accounting for 38.3–41.6% and 15.6–15.8%, respectively. Additionally, fine particles (PM2.5) were the major component of secondary atmospheric aerosols, SO2 and NOX were the precursor to the formation of sulfate and nitrate PM, including acid and nonacid aerosols in the atmosphere.3 Studies have shown that the major sources of the fine aerosols in mega-cities was secondary formation of coal/biomass-burning products, which have been recently recognized as a dominant cause for the air pollution.4

It has long been known that air pollution is harmful to human health, especially to respiratory health, as many epidemiological and experimental studies have been conducted into its effects. Among all kinds of respiratory diseases, chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. According to WHO statistics, 65 million people have moderate to severe COPD and more than 3 million people died of it in 2005, which corresponds to 5% of all deaths globally. Meanwhile, air pollution has been verified as a cause for the development of COPD for more than fifty years.5 A study in Hong Kong showed the relationship between air pollutants and hospital admissions due to acute exacerbation of COPD from 2000 to 2004, and observed that the relative risk of hospital admissions for every 10 mg m–3 increase in SO2, NO2 and PM2.5 were 1.007, 1.026 and 1.031, respectively.6 Evidence from patients with COPD suggested that they experienced mild reductions in lung function in association with increased pollution (such as PM10, NO2) levels.7,8 Therefore, air pollution is currently considered an important cause of COPD exacerbation.

As a type of obstructive lung disease, COPD is characterized by chronically poor airflow.9 More importantly, pulmonary hypertension (PH) was known as a common clinical manifestation of COPD and is an increase of blood pressure in the lung vasculature, leading to shortness of breath, dizziness, fainting and other symptoms.10 The molecular mechanism of PH is not yet known, but it is believed that the endothelial dysfunction results in a decrease in the synthesis of endothelium-derived vasodilators.11 Endothelin-1 (ET-1) and endothelial nitric oxide synthase (eNOS) were the major biomarkers of PH.12 Despite these new insights, illumination of the molecular mechanisms of PH remains.

MicroRNAs (miRNAs) are highly conserved, small, non-coding RNAs of 19–24 nucleotides in length that have recently been shown to act as important regulators of gene expression at the post-transcriptional level, and they played key roles in the regulation of crucial biological processes such as cell growth and apoptosis.13,14 Both in vivo and in vitro experimental results have demonstrated that miRNAs are closely related to respiratory injuries.15,16 In recent years, reports have begun to emerge suggesting that environmental contaminants can indeed change the expression profile of miRNAs; and the changes of these miRNAs may be critical for regulating biological and physiological responses to air pollutants.17 In 2010, a study showed that PM exposure upregulated the expressions of miR-222 and miR-21 through elevation reactive oxygen species (ROS) production.18 Another study showed that almost two thirds of detected miRNAs were changed at least 1.5-fold in human bronchial epithelial cells treated with diesel exhaust particles.19 It has been proposed that environmental cigarette smoke induced the alterations in miRNAs in the lungs of rats.14 All in all, miRNAs, as important epigenetic factors, are likely to play important roles in lung disease etiology and have received more and more attention.

Considering ambient air pollution contains many kinds of toxicants, and the combined toxicity of these pollutants, it is extremely important to find the biomarkers indicating PH, and even COPD, after exposure. Given the significant role of miRNAs in tuning and buffering gene expressions in a number of critical cellular processes, we hypothesize that miRNAs may be responsible for certain gene expression and functional impairment of PH induced by co-exposure of air pollutants. The present study aims to identify miRNAs homologous with humans after coal-burning air pollutants co-exposure, and to find marked miRNA-targeted gene expression and functional alteration contributing to PH. Here, we treated male C57BL/6 mice with SO2, NO2 and PM2.5 at different concentrations to mimic the combined exposure of coal-burning air pollution; and unveiled that miR-338-5p modulates PH-like injuries after SO2, NO2 and PM2.5 co-exposure through targeting the hypoxia-inducible factor 1α (HIF-1α)/four-and-a-half LIM (Lin-11, Isl-1 and Mec-3) domain 1 (Fhl-1) pathway, and may be used as an early biomarker underlying the COPD exacerbation and even occurrence in polluted areas.

2. Material and methods

2.1. PM2.5 sampling

The sampling point was located in Shanxi University (112°57′E longitude, 37°73′N latitude), Taiyuan City, Shanxi Province of China. Meanwhile, the sampling site was surrounded by highways, schools, three villages inside the city and a railway station within two kilometers. Samplings were carried out during the period from November 2013 to February 2014. PM2.5 were collected onto quartz filters (Φ 90 mm, Munktell, Sweden) using PM air samplers (TH-150CIII, Wuhan, China). PM2.5 suspension was prepared through soaking filters with PM2.5 in ultrapure water for 30 min, followed by vortexing for 5 min and sonication for 30 min. Then, the PM2.5 suspension was processed with the vacuum freeze-drying technique. The dried samples were diluted with sterilized 0.9% physiological saline and swirled for 10 min before using for animal experiments, and aqueous suspension was pooled and frozen at –20 °C.

2.2. Animal treatment

Male C57BL/6 mice (7–8 weeks old) were obtained from Beijing HFK Bioscience Co., Ltd (Beijing, China). Mice were housed under standard conditions (24 ± 2 °C, 50 ± 5% humidity, and 12 : 12 h light : dark cycle). After one week of adaptation, the mice were randomly divided into three groups. For the control group (C), the mice were exposed to filtered air (6 h per day, 28 days) and saline was injected directly into the lung by pharyngeal aspiration every other day. For the lower concentration group (L), mice were treated with 0.5 mg m–3 SO2 and 0.2 mg m–3 NO2via dynamic inhalation simultaneously (6 h per day, 28 days) followed by pharyngeal aspiration of PM2.5 1 mg kg–1 every other day. For the higher concentration group (H), mice were exposed to 3.5 mg m–3 SO2, 2 mg m–3 NO2 and 3 mg kg–1 PM2.5 using the same protocol. SO2 and NO2 gases were diluted with fresh air at the intake port of the chambers to yield the desired concentrations, and the diluted gases were evenly distributed across the whole chamber by two perforated gas radiant plates, with one located in the intake port and the other connected to a gas outlet matched with an aspirator pump. The concentrations of SO2 and NO2 within the chambers were measured every 30 min using pararosaniline hydrochloride spectrophotometry at 577 nm and the Saltzman colorimetric method at 540 nm, respectively. When not being treated, the animals had free access to water and standard feed. Mice were sacrificed 18 h after the last exposure. The lungs were excised, quick frozen in liquid nitrogen, and then stored at –80 °C. All animal experimental procedures were performed in accordance with the Guidelines for the Care and Use of Laboratory Animals of the Chinese Animal Welfare Committee. The protocol was approved by the Shanxi University's Institutional Animal Care and Use Committee.

2.3. miRNA microarray analysis

Briefly, total RNA was extracted, purified and the integration according to previous studies was inspected. Then miRNAs were label-hybridized according to the manufacturer's instructions of miRNAs Complete Labeling and Hyb Kit (Agilent technologies, Santa Clara, CA, US). After hybridization, slides were washed in staining dishes (Thermo Shandon, Waltham, MA, US) with a Gene Expression Wash Buffer Kit (Agilent technologies, Santa Clara, CA, US) and scanned by an Agilent Microarray Scanner (Agilent technologies, Santa Clara, CA, US) and Feature Extraction software 10.7 (Agilent technologies, Santa Clara, CA, US). Gene Spring Software 11.0 (Agilent technologies, Santa Clara, CA, US) was used to analyze the raw data. Based on the results, we considered miRNAs with p < 0.05 as differentially expressed miRNAs.20

2.4. Bioinformatics analysis

miRNAs from human sources that changed significantly were analyzed by bioinformatics, and the potential target genes of miR-142-5p, miR-338-5p and miR-450b-3p were screened by TargetScan (http://www.targetscan.org/mmu_61/), miRanda (; http://www.microrna.org/microrna/home.do/), PicTar (; http://pictar.mdc-berlin.de/), MirTarget2 (; http://mirdb.org/miRDB/) and PITA5 (; http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html). Only the target genes that were included in all databases were selected as the target genes. Genomatix software (; http://www.genomatix.de) was used for searching the network of target genes. This software aimed at understanding gene regulation at the molecular level, representing a central part of systems biology.

2.5. Real-time quantitative reverse transcription-PCR (RT-PCR) and immunoblot analysis

Total RNA was extracted from the lungs of mice according to the manufacturer's protocol of miRNeasy Mini Kit (Qiagen Biotechnology Co., Ltd, Dalian). Then, the extracted RNAs were transcribed to complementary DNA (cDNA) using a reverse transcription kit (Qiagen Biotechnology, Germany). Real-time quantitative PCR (qPCR) was performed on a Real-Time PCR qTOWER 2.2 (Analytik Jena AG, Jena, Germany) using mmu-miR-U6 as an internal control according to the instructions of the miScript SYBR Green PCR kit (Qiagen Biotechnology, Germany). Briefly, each 20 μL PCR reaction contained 2 μL cDNA, 10 μL 10× QuantiTect SYBR Green, 4 μL RNase Free H2O, 2 μL 10× miScript Universal Primer and 2 μL 5 μmol L–1 specific primers (Invitrogen). Mmu-miR-338-5p, mmu-miR-450b-3p, mmu-miR-142-5p and mmu-miR-U6 used a three-step method, their reaction conditions were as follows: after 15 min at 95 °C, 40 cycles were performed at 95 °C for 20 s, respectively, with annealing temperatures (58.2 °C (mmu-miR-338-5p), 55 °C (mmu-miR-142-5p) and 60.6 °C (mmu-miR-U6)) for 20 s, and 72 °C for 30 s. Primers sequences used for real-time RT-PCR are shown in Table S1.†

Protein was extracted and quantified according to a previously reported method.21 Then, 100 μg of total proteins was separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a nitrocellulose membrane. The membranes were blocked with 3% bovine serum albumin and probed with the indicated antibodies specific for mice ET-1, eNOS, HIF-1α, Fhl-1 (1 : 200, Beijing Biosynthesis Biotechnology, China) and β-actin (1 : 1000, Cell Signaling Technology, USA) at 4 °C overnight, and then exposed to fluorescently labeled secondary antibody (1 : 2000) (IRDye 800CW Goat anti-Rabbit IgG (H + L), LI-COR) for 2 h. Blots were scanned using a LI-COR Odyssey® infrared fluorescence instrument.

2.6. Dual-luciferase reporter gene analysis

Dual-luciferase reporter gene analysis was used to detect whether miR-338-5p could bind to HIF-1α mRNA. The following primers were used to amplify the 3′-UTR of HIF-1α: 5′-CCGCTCGAGGCGTTTCCTAATCTCATTCCTTTTG-3′ (forward) and 5′-ATAAGAATGCGGCCGCTAAGCTGGAAGGTTTGTGGTGTT-3′ (reverse). A construct possessing mutations disrupting the putative miR-338-5p binding sites in the 3′-UTR of HIF-1α was prepared using a KOD Plus neo DNA polymerase (ToYoBo) with the following primers: 5′-TCTGTAGTTGTGGAAGCTTATGCTATATAACAGTAATTGATTATGAAACATAAATGT-3′ (forward) and 5′-ACATTTATGTTTCATAATCAATTACTGTTATATAGCATAAGCTTCCACAACTACAGA-3′ (reverse). The PCR products were cloned into the site of psiCHECK-2 vector (Promega) downstream of the luciferase gene. HEK293T were plated into 24-well plates (2 × 104 per well) in DMEM containing 10% FBS for 24 h, then, cells were co-transfected with 100 ng mL–1 of either the wild-type or mutant 3′-UTR vector and 50 nM of either the miR-338-5p mimic or 100 nM miR-338-5p inhibitor using Lipofectamine 2000 (Invitrogen). After 48 h, luciferase activity was determined according to the instruction of the dual luciferase reporter kit (Promega).22

2.7. Hematoxylin–eosin (H&E) staining and transmission electron microscopy (TEM) observation

For H&E staining, the sections of paraffin-embedded tissues (5–6 μm-thick) were de-paraffinized and stained with hematoxylin and eosin according to our previous study and observed by light microscopy. We used the Image Pro Plus for measurement of total vessel area (μm2), luminal area (μm2) and inner circumference (μm). Medial area (μm2) and medial thickness (μm) were calculated according to a previous study.23 For TEM observation, about 1 mm3 pieces were fixed, En bloc stained, dehydrated and embedded in beam capsules. 70–80 nm-thick sections of embedded tissue were collected onto grids, stained with uranyl acetate and lead citrate and then observed by TEM (JEOL, JEM-1011, Japan).

2.8. Lung function determination

An AniRes 2005 Lung Function system (Bestlab 2.0, Beijing, China) was used to determine the forced expiratory volume in 0.1 second (FEV0.1)/forced vital capacity (FVC), which is an indicator of airway limitation. Briefly, the mice were anesthetized with 95 mg kg–1 pentobarbital sodium (solarbio) followed by tracheal intubation. A cannula implanted surgically in the trachea established the connection between mouse and computer-controlled ventilator. Mechanical ventilation parameters were set following these instructions: the respiratory rate was 90 min–1 and the time ratio of expiration/inspiration was 20 : 10; the animal weights were the average value of each group; the tidal volume was 5 mL kg–1 and the stress in the end of expiration was equal to the pressure of 8 cm H2O (1 cm H2O–0.098 kPa) regulated by the regulator of the water seal bottle. Then, we calculated the value FEV0.1/FVC using the AniRes 2005 software.24

2.9. Statistical analyses

Results were expressed as mean ± SE. The data were analyzed by one-way ANOVA followed by LSD test to identify significant differences between the different concentration groups using origin 8. Results with p < 0.05 were considered as significant.

3. Results

3.1. SO2, NO2 and PM2.5 co-exposure induce airway limitation

In the present study, FEV0.1/FVC (for mice) was detected in the control group and the higher concentration group after co-exposure. As shown in Fig. 1A, it is obvious that the FEV0.1/FVC in the treated mice were significantly decreased after co-exposure to air pollutants (0.75 for control group and 0.56 for higher concentration group, p < 0.05). Worsening of FEV0.1/FVC suggests airway narrowing, and the results were confirmed by histopathological alteration of the airway in Fig. 1B. Apparently, the histopathological changes of distal bronchial in the higher concentration group were severe with prominent airway mucosa thickening, epithelial disruption and denudement and a pronounced airway obstruction. Furthermore, blinded quantification of medial width, medial area/total area and medial wall area/luminal area also demonstrated the narrowing of airways (Fig. 1C). Some relevant relationships between airway limitation and PH was suggested in previous studies. The biomarkers of PH were detected in the next step.

Fig. 1. SO2, NO2 and PM2.5 co-exposure induce airway limitation. (A) Airway limitation was determined by FEV0.1/FVC (n = 6). Data shown as mean ± SE. (B) Representative histopathological images of H&E staining in the lungs of mice (n = 3). Left: Airway of mice lungs from control group; right: following co-exposure to higher concentration air pollutants. Arrow: airway alteration of mice lungs. Bar = 20 μm. (C) Blinded quantification and statistical analysis of bronchial wall thickness, bronchial wall area/total area and bronchial wall area/luminal area. *p < 0.05, **p < 0.01 and ***p < 0.001 vs. control group.

Fig. 1

Open in a new tab

3.2. SO2, NO2 and PM2.5 co-exposure cause PH-related protein alteration and morphological abnormality

ET-1 and eNOS are crucial mediators for establishing PH.25 As shown in Fig. 2, after co-exposure to three air pollutants, the expression of ET-1 and eNOS showed a dose–response effect. In the higher concentration group, ET-1 expression was elevated 1.38-fold compared with the control group (p < 0.05), while eNOS expression were statistically down-regulated to 0.65-fold of the control (p < 0.05). Pulmonary vascular remodeling is one of the characteristic pathological changes of PH and remodeling of the pulmonary vasculature occurs primarily in small vessels (<500 μm diameter).26 Following this, pathological and ultrastructural alteration in the lung was further detected. After co-exposure, the tunica media was hypertrophied with the extension of vascular smooth muscle from the muscular pulmonary arteries into the wall of this arteriole (Fig. 3A). As shown in Fig. 3B, multiple, random measurements, blinded quantification of the vessel wall and lumen area and statistical analysis further indicated the arteriolar remodeling and narrowing of the arteriolar lumen. The results were compatible with those observed in rat lung for the pulmonary hypertension model.27,28 Also, based on ultrastructural examination, the observed endothelial dysfunction included abnormal capillary profiles, abundant pinocytotic vesicles in endotheliocyte and deposition of collagen fibers in the widened subendothelial space (Fig. 4); and similar results were also reported in pigs with hypoxic-induced pulmonary hypertension and in patients with other forms of pulmonary hypertension.29,30

Fig. 2. Effects of SO2, NO2 and PM2.5 co-exposure on ET-1 (A) and eNOS (B) expression (n = 6). Data shown as mean ± SE. *p < 0.05 vs. control group.

Fig. 2

Open in a new tab

Fig. 3. Representative histopathological images and statistical data of H&E staining in the lungs after SO2, NO2 and PM2.5 co-exposure. (A) Representative histopathological of H&E staining (n = 3). Left: Pulmonary vasculature from control group; middle: following co-exposure to low concentration air pollutants; right: following co-exposure to high concentration air pollutants. Arrow: site of pulmonary vasculature. Bar = 20 μm. (B) Blinded quantification and statistical analysis of media width, media area/total area and media area/luminal area. Data shown as mean ± SE. *p < 0.05 vs. control group.

Fig. 3

Open in a new tab

Fig. 4. Transmission electron micrographs of the lungs after SO2, NO2 and PM2.5 co-exposure (n = 2). Left: Following co-exposure to low concentration air pollutants; right: following co-exposure to high concentration air pollutants. Arrow: capillary profiles; EC: endothelial cells; Coll: collagen fibers. Bar = 2 μm.

Fig. 4

Open in a new tab

3.3. miRNAs are involved in PH-like injuries after SO2, NO2 and PM2.5 co-exposure

After co-exposure to SO2, NO2 and PM2.5 for 4 weeks, three data sets from the three study groups were obtained by miRNA array analysis. The raw data revealed a significantly high correlation (R > 0.94) among the three biological replicates for each group by using Pearson correlation analysis, suggesting good reproducibility of samples (see Table S2, ESI†). In this study, 1247 miRNAs were detected in miRNA array analysis. In the higher concentration group, expressions of 18 miRNAs changed significantly, of which 6 miRNAs were up-regulated and 12 miRNAs were down-regulated (p < 0.05). As for the lower concentration group, there were 3 significantly down-regulated miRNAs and 3 significantly up-regulated miRNAs (p < 0.05) (Table S3†). In these significantly changed miRNAs, miR-338-5p, miR-450b-3p and miR-142-5p were homologous with humans in the higher concentration group, none were found in lower concentration group. Although the majority of the miRNAs are highly conserved across different species, we chose miRNAs homologous with humans for further study to ensure the accuracy of our next step. In order to validate the data of miRNA array analysis, the expression levels of three miRNAs, miR-338-5p, miR-450b-3p and miR-142-5p were assayed by RT-PCR. The results showed that the expression variations of miR-338-5p and miR-450b-3p were consistent with the array analysis data; miR-142-5p was different from the array analysis data (Fig. 5).

Fig. 5. Comparison of expression changes between microarray and RT-PCR. (A) Hierarchical clustering of miRNAs detected in the control and high concentration group (n = 3). Each column represents one biological replicate. Samples are labeled as shown with the tab beside the heatmap. “C1”, “C2” and “C3” represent replicate 1, 2 and 3 of the control group, respectively. “H1”, “H2” and “H3” represent replicate 1, 2 and 3 of the high concentration group, respectively. Colors represent expression levels of each individual miRNA: black, mean vector for all miRNAs expression levels; red, up-regulation compared with mean vector; green, down-regulation compared with mean vector. (B) Comparison of expression changes of miRNAs that were homologous with human between microarray and RT-PCR (n = 6). Data shown as mean ± SE. *p < 0.05 vs. the control group determined by microarray. #p < 0.05, ##p < 0.01 and ###p < 0.001 vs. the control group as determined by RT-PCR.

Fig. 5

Open in a new tab

3.4. miR-338-5p targets the HIF-1α/Fhl-1 pathway through directly binding to the 3′-UTR of HIF-1α mRNA in SO2, NO2 and PM2.5 co-exposure produced PH-like injuries

Several research groups have reported miR-338-5p was related to some lung diseases.31,32 Based on related literature, also considering the consistency of the array analysis data and RT-PCR data, we predicted potential targets for miR-338-5p using five bioinformatics software, namely TargetScan, miRanda, PicTar, MirTarget2 and PITA. Only potential targets included in all five bioinformatics software were chosen for next analysis (Table S4†). It is intriguing to find out the relationship among these target genes. Fig. 6 shows the relationship among the target genes of miR-338-5p obtained by Genomatix software, and HIF-1α was the central hub of the network.

Fig. 6. The relationship among the target genes of miR-338-5p obtained by Genomatix software.

Fig. 6

Open in a new tab

Previous studies had showed that HIF-1α played a critical role in PH.33 Meanwhile, we all know that miRNAs could bind to the 3′-untranslated regions (3′-UTR) of target mRNAs and then negatively regulate the expression of target genes. Therefore, we hypothesized that miR-338-5p might mediated PH-like injuries through binding to the 3′-UTR of HIF-1α after SO2, NO2 and PM2.5 co-exposure. To clarify the implication, we used Targetscan software to screen the 3′-UTR region of HIF-1α for mREs (5′-ATATTGT-3′) and identified the binding sites of miR-338-5p. Fig. 7A showed that miR-338-5p inhibited HIF-1α 3′-UTR luciferase activity, whereas miR-338-5p failed to inhibit luciferase activity with the mutated luciferase construct, which suggested that miR-338-5p could bind to the 3′-UTR of HIF-1α. We pretreated HEK293T cells with a miR-338-5p inhibitor that efficiently reduced the binding of miR-338-5p and HIF-1α 3′-UTR. Following this, we further detected the protein expression of HIF-1α, and found that the HIF-1α protein was significantly higher in the higher concentration group as compared to normal counterparts (p < 0.05), but no significant changes were observed in the lower concentration group (Fig. 7B). This data suggests that the SO2, NO2 and PM2.5 co-exposure down-regulated miR-338-5p and stimulated HIF-1α expression via miR-338-5p binding to the 3′-UTR of HIF-1α mRNA. It has been suggested that Fhl-1 and the HIF-1α/Fhl-1 pathway may play an important role in PH.34,35 Therefore, we investigated the protein expression of Fhl-1 and a significantly higher protein expression was observed in the higher concentration group (p < 0.01) (Fig. 7C).

Fig. 7. Dual-luciferase reporter gene analysis and the expression of HIF-1α and Fhl-1. (A) The scheme of the luciferase assay using dual-luciferase reporter experiments to evaluate the direct inhibition of miR-338-5p on HIF-1α protein expression (n = 6). HIF-1α contains a conserved 3′-UTR sequence (positions 819–825) that perfectly complements the miR-338-5p seed sequence (both are shown in blue). The mutant HIF-1α 3′-UTR contains mutations in the miR-338-5p binding site that disrupts base pairing (indicated by red). The data of different effects of miR-338-5p on HIF-1α 3′-UTR and its mutant shown as mean ± SE. miR-338-5p can efficiently inhibit the luciferase expression by binding to HIF-1α 3′-UTR, but has no effect on the luciferase expression when the binding sites were mutated on HIF-1α 3′-UTR. (B) Effect of co-exposure to SO2, NO2 and PM2.5 on the protein levels of HIF-1α (n = 6). (C) Effect of co-exposure to SO2, NO2 and PM2.5 on the protein levels of Fhl-1 (n = 6). NC was the negative control. Data shown as mean ± SE. *p < 0.05 and ***p < 0.001 vs. control group.

Fig. 7

Open in a new tab

4. Discussion

Since the economic reforms began in 1978, China has undergone very rapid economic growth followed by an expansion of the urban population. This expansion has caused enormous energy consumption and the growth of coal consumption, which has resulted in serious environmental pollution, with tremendous increases in pollutant emission during this period. Secondary aerosol is formed from the emission of gases such as SO2, NOx, and NH3 and turn into fine particulates in the atmosphere through chemical reactions or condensation.36 Considering that scholars seldom study the combined toxicity of air pollutants and the reactions among them, we treated adult mice to SO2, NO2 and PM2.5 to unveil the relationship between air pollutants from coal burning and pulmonary injuries in the perspective of miRNAs.

COPD is not a single disease, but an umbrella term used to describe chronic lung diseases that cause limitations in lung airflow. The pulmonary function test is one of the most visual indices in diagnosis of these diseases.37 Some studies have indicated that ambient air pollution was associated with COPD.38,39 In the clinical setting, a reduction in FEV1/FVC (for human) was an indicator of airflow limitation.40 In the present study, FEV0.1/FVC was detected and the value in the higher concentration group was decreased after co-exposure to air pollutants compared with the control. In another study, FEV0.1/FVC was below 0.7 in the cigarette smoke exposure mice, which were picked out as the COPD group.41 Apparently, co-exposure to these major air pollutants from coal burning has reduced the lung function of mice. Previous studies reported that 50 COPD patients with severe airflow limitation had an average Ppa value of 26 mmHg (PH defined as Ppa ≈ 20 mmHg).42 In a subsequent study of 175 patients with COPD, a prevalence of 35% PH was observed. Therefore, these results of pulmonary function guide us to speculate the possibility of PH.43

In the next step, we screened for the biomarkers of PH. Endothelial dysfunction of pulmonary vascular seems to play an integral role in the pathogenesis of PH.30 The balance between constriction and dilatation of pulmonary vessels is maintained by a number of small molecular mediators.25 Many kinds of endothelial vasoactive mediators, including nitric oxide (NO), ET-1, prostacyclin and serotonin, have been increasingly recognized in patients with PH.11 NO produced by eNOS has vasodilator properties.44 Endothelium-derived ET-1 can counter vasodilation.45 Therefore, the expressions of these two biomarkers of PH were detected in the present study. As we expected, the expression of eNOS was decreased and that of ET-1 was increased. The results were in agreement with the pathological and ultrastructural alteration. Similar results were found in children living in Mexico, that chronic exposure to a complex mixture of air pollutants was associated with a significant elevation of pulmonary arterial pressures (measured by echocardiography) and with elevated plasma ET-1 levels.46 Another study had also showed that NO-responsiveness in intrapulmonary arteries was impaired after exposure of rats to concentrated ambient PM.47 Taken together, co-exposure to air pollutants may cause the symptoms of PH-like injuries in mice.

During the past decades, studies witnessed miRNAs as a hot area for many kinds of diseases. Among these studies, it was reported that miRNAs were related to respiratory diseases, such as allergic airway inflammation,48,49 COPD,50 asthma51,52 and lung cancer.32,53–56 These findings support the need for a better understanding of miRNAs expression and their effects on respiratory injury. Therefore, we sought to clarify the potential mechanism by which air pollutants co-exposure promotes PH-like injuries based on a perspective of miRNAs modulation. The results of miRNA array analysis and RT-PCR screened two miRNAs (miR-338-5p and miR-450b-3p) that were homologous with humans in the higher concentration group. miR-338-5p was found to be related to the proliferation of pulmonary fibroblasts in rats with idiopathic pulmonary fibrosis and played a critical role in lung cancer initiation and progression.31,32 Nevertheless, to the best of our knowledge, few studies have been conducted on miR-450b-3p-induced changes in lung diseases. Besides, the target genes of these two miRNAs were predicted through five bioinformatics software. Obviously, the number of target genes of miR-338-5p was far more than that of miR-450b-3p (Tables S4 and S5†) and none of the miR-450b-3p target genes were reported to participate in PH. Moreover, among these miR-338-5p target genes, HIF-1α was the central gene of the target genes network. It is now clear that HIF-1α, which was responsive to hypoxia, can also be activated by a number of non-hypoxic stimuli including air pollution, lipopolysaccharide, ROS and so on.57,58 According to related studies, HIF-1 can regulate more than 100 genes and it-mediated pathways influence metabolic adaptation, erythropoiesis, angiogenesis and vascular tone, cell growth and differentiation; and thus, is critical in development, physiology and disease.59 Recent studies have provided evidence that miR-34a and miR-199a-5p participated in the pathogenesis of COPD, and these miRNAs may also affect the HIF-1α-dependent lung structure maintenance program;50 more importantly, miR-204 and miR-22 have been indicated to participated in PH.60,61 HIF-1 could activate the transcription of genes encoding several factors such as ET-1, erythropoietin, iNOS and vascular endothelial growth factor, which are important in the development of hypoxic PH.62 It is thus reasonable to assume that miR-338-5p could bind to the 3′-UTR of HIF-1α and then induce the symptoms of PH-like injures. As we expected, the results of the bioinformatics software and dual-luciferase reporter gene analysis together suggested that miR-338-5p could bind to 3′-UTR of HIF-1α. The up-regulation of HIF-1α further suggests the same point because of the negative regulation role of miRNAs in its target genes expression. Fhl-1 has also been identified as one of the most highly upregulated proteins in hypoxia induced PH in mice, human idiopathic pulmonary arterial hypertension (IPAH) and as a key regulator of human pulmonary arterial smooth muscle cells (PASMC) proliferation and migration.10 Also, Fhl-1 was identified as a novel protein regulated in various forms of PH through protein screening.34 Previous studies have shown that Fhl-1 expression was regulated by HIF-1α in PASMC of pulmonary arteries and arterioles, a key site of vascular remodeling in PH.34 The feedback loop of HIF-1α and Fhl-1 may serve to regulate HIF activity under hypoxia conditions.35 These studies, combined with the findings of previous studies, provide a contributing mechanism that miRNA modulates PH-like injuries through targeting the HIF-1α/Fhl-1 pathway after co-exposure to a higher concentration of air pollutants.

5. Conclusion

The results in our study suggested that miR-338-5p modulated PH-associated endothelial dysfunction of pulmonary vascular and respiratory function by SO2, NO2 and PM2.5 co-exposure through directly binding to the 3′-UTR of HIF-1α mRNA and targeting the HIF-1α/Fhl-1 pathway (Fig. 8). The novel regulatory circuit formed by HIF-1α and Fhl-1 provides additional insight into the interaction between miRNAs and signaling molecules for air pollutants co-exposure-induced PH, and implicates it as an early biomarker for COPD occurrence in coal-burning areas.

Fig. 8. The mechanism of PH-like injuries after co-exposure to SO2, NO2 and PM2.5. Air pollutants could down-regulate the expression of miR-338-5p and abnormal expression of miR-338-5p led to a change in the biological response of PH by targeting the HIF-1α/Fhl-1 pathway.

Fig. 8

Open in a new tab

Conflict of interests

The authors declare that there are no conflicts of interest in the present work.

Abbreviations

COPD

Chronic obstructive pulmonary disease

PH

Pulmonary hypertension

ET-1

Endothelin-1

eNOS

Endothelial nitric oxide synthase

miRNA

microRNA

HIF-1α

Hypoxia-inducible factor 1α

Fhl-1

Four-and-a-half LIM (Lin-11, Isl-1 and Mec-3) domain 1

SO2

Sulfur dioxide

NOX

Nitrogen oxides

AECOPD

Acute exacerbation of chronic obstructive pulmonary disease

PASMCs

Pulmonary artery smooth muscle cells

ROS

Reactive oxygen species

ECS

Environmental cigarette smoke

FEV0.1

Forced expiratory volume in 0.1 second

FVC

Forced vital capacity

FEV1

Forced expiratory volume in one second

3′-UTR

3′-Untranslated regions

LSD

Least significant difference

PB

Phosphate buffer

TEM

Transmission electron microscopy

H&E

Hematoxylin–eosin staining

cDNA

Complementary DNA

LPS

Lipopolysaccharide

TNF-α

Tumor necrosis factor-α

NO

Nitric oxide

Supplementary Material

Click here for additional data file. (161.2KB, pdf)

Acknowledgments

This study was supported by National Science Foundation of PR China (NSFC, no. 91543203, 21477070, 21377076, 21307079, 21222701), Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP, no. 20121401110003, 20131401110005), and Research Project Supported by Shanxi Scholarship Council of China (no. 2015-006).

Footnotes

†Electronic supplementary information (ESI) available. See DOI: 10.1039/c6tx00257a

References

  1. Chan C. K., Yao X. Atmos. Environ. 2008;42:1–42. [Google Scholar]
  2. Shen G., Wang W., Yang Y., Zhu C., Min Y., Xue M., Ding J., Li W., Wang B., Shen H. Atmos. Environ. 2010;44:5237–5243. doi: 10.1016/j.atmosenv.2010.08.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. EPA, National Air Pollutant Emission Trends 1900–1998, US Environmental Protection Agency, 2000. [Google Scholar]
  4. Wang Y., Zhuang G., Tang A., Yuan H., Sun Y., Chen S., Zheng A. Atmos. Environ. 2005;39:3771–3784. [Google Scholar]
  5. Ghozikali M. G., Mosaferi M., Safari G. H., Jaafari J. Environ. Sci. Pollut. Res. 2015;22:2817–2823. doi: 10.1007/s11356-014-3512-5. [DOI] [PubMed] [Google Scholar]
  6. Ko F. W., Tam W., Wong T. W., Chan D. P., Tung A. H., Lai C. K., Hui D. S. Thorax. 2007;62:780–785. doi: 10.1136/thx.2006.076166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Anderson H., Spix C., Medina S., Schouten J., Castellsague J., Rossi G., Zmirou D., Touloumi G., Wojtyniak B., Ponka A. Eur. Respir. J. 1997;10:1064–1071. doi: 10.1183/09031936.97.10051064. [DOI] [PubMed] [Google Scholar]
  8. Schikowski T., Sugiri D., Ranft U., Gehring U., Heinrich J., Wichmann H.-E., Kramer U. Respir. Res. 2005;6:152. doi: 10.1186/1465-9921-6-152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Snider G. L. Am. J. Respir. Crit. Care Med. 2003;167:678–683. doi: 10.1164/rccm.200203-204PP. [DOI] [PubMed] [Google Scholar]
  10. Yue J., Guan J., Wang X., Zhang L., Yang Z., Ao Q., Deng Y., Zhu P., Wang G. Lab. Invest. 2013;93:748–759. doi: 10.1038/labinvest.2013.63. [DOI] [PubMed] [Google Scholar]
  11. Budhiraja R., Tuder R. M., Hassoun P. M. Circulation. 2004;109:159–165. doi: 10.1161/01.CIR.0000102381.57477.50. [DOI] [PubMed] [Google Scholar]
  12. Barbera J., Peinado V., Santos S. Eur. Respir. J. 2003;21:892–905. doi: 10.1183/09031936.03.00115402. [DOI] [PubMed] [Google Scholar]
  13. Lai L., Song Y., Liu Y., Chen Q., Han Q., Chen W., Pan T., Zhang Y., Cao X., Wang Q. J. Biol. Chem. 2013;288:7956–7967. doi: 10.1074/jbc.M112.445429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Izzotti A., Calin G. A., Arrigo P., Steele V. E., Croce C. M., De Flora S. FASEB J. 2009;23:806–812. doi: 10.1096/fj.08-121384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Huang C., Xiao X., Chintagari N. R., Breshears M., Wang Y., Liu L. BMC Med. Genomics. 2014;7:46. doi: 10.1186/1755-8794-7-46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Tamarapu Parthasarathy P., Galam L., Huynh B., Yunus A., Abuelenen T., Castillo A., Kollongod Ramanathan G., Cox Jr. R., Kolliputi N. Biochem. Biophys. Res. Commun. 2012;426:203–208. doi: 10.1016/j.bbrc.2012.08.063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jardim M. J. Mutat. Res. 2011;717:38–45. doi: 10.1016/j.mrfmmm.2011.03.014. [DOI] [PubMed] [Google Scholar]
  18. Bollati V., Marinelli B., Apostoli P., Bonzini M., Nordio F., Hoxha M., Pegoraro V., Motta V., Tarantini L., Cantone L., Schwartz J., Bertazzi P. A., Baccarelli A. Environ. Health Perspect. 2010;118:763–768. doi: 10.1289/ehp.0901300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Jardim M. J., Fry R. C., Jaspers I., Dailey L., Diaz-Sanchez D. Environ. Health Perspect. 2009;117:1745–1751. doi: 10.1289/ehp.0900756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hu J., Wang Z., Tan C.-J., Liao B.-Y., Zhang X., Xu M., Dai Z., Qiu S.-J., Huang X.-W., Sun J. Transplantation. 2013;95:991–999. doi: 10.1097/TP.0b013e31828618d8. [DOI] [PubMed] [Google Scholar]
  21. Yan W., Ji X., Shi J., Li G., Sang N. Environ. Res. 2015;138:416–424. doi: 10.1016/j.envres.2015.02.022. [DOI] [PubMed] [Google Scholar]
  22. Zha W., Cao L., Shen Y., Huang M. PLoS One. 2013;8:e74175. doi: 10.1371/journal.pone.0074175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Said S. I., Hamidi S. A., Dickman K. G., Szema A. M., Lyubsky S., Lin R. Z., Jiang Y. P., Chen J. J., Waschek J. A., Kort S. Circulation. 2007;115:1260–1268. doi: 10.1161/CIRCULATIONAHA.106.681718. [DOI] [PubMed] [Google Scholar]
  24. Wen W., Wang A.-m., Liu D.-l., Zhang Y.-b., Yao L.-q., Lai G.-x. Chronic Obstruct. Pulm. Dis.: Curr. Concepts Pract. 2013;10:657–666. [Google Scholar]
  25. Shujaat A., Minkin R., Eden E. Int. J. Chronic Obstruct. Pulm. Dis. 2007;2:273. [PMC free article] [PubMed] [Google Scholar]
  26. Martin K. B., Klinger J. R., Rounds S. I. Respirology. 2006;11:6–17. doi: 10.1111/j.1440-1843.2006.00778.x. [DOI] [PubMed] [Google Scholar]
  27. Lam C.-F., Peterson T. E., Croatt A. J., Nath K. A., Katusic Z. S. Am. J. Physiol.: Heart Circ. Physiol. 2005;289:H2334–H2341. doi: 10.1152/ajpheart.00375.2005. [DOI] [PubMed] [Google Scholar]
  28. Le Cras T. D., Hardie W. D., Deutsch G. H., Albertine K. H., Ikegami M., Whitsett J. A., Korfhagen T. R. Am. J. Physiol.: Lung Cell. Mol. Physiol. 2004;287:L718–L729. doi: 10.1152/ajplung.00084.2004. [DOI] [PubMed] [Google Scholar]
  29. Wright J., Levy R., Churg A. Thorax. 2005;60:605–609. doi: 10.1136/thx.2005.042994. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Seeger W., Pullamsetti S. S. Pulm. Circ. 2013;3:128. [PMC free article] [PubMed] [Google Scholar]
  31. Zhang H., Liu X., Chen S., Wu J., Ye X., Xu L., Chen H., Zhang D., Tan R., Wang Y. J. Ethnopharmacol. 2010;131:165–173. doi: 10.1016/j.jep.2010.06.022. [DOI] [PubMed] [Google Scholar]
  32. Hubaux R., Becker-Santos D. D., Enfield K. S., Lam S., Lam W. L., Martinez V. D. Metabolomics. 2012;2:1000108. doi: 10.4172/2153-0769.1000108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Tuder R. M., Voelkel N. F. Antioxid. Redox Signaling. 2002;4:833–843. doi: 10.1089/152308602760598990. [DOI] [PubMed] [Google Scholar]
  34. Kwapiszewska G., Wygrecka M., Marsh L. M., Schmitt S., Trösser R., Wilhelm J., Helmus K., Eul B., Zakrzewicz A., Ghofrani H. A. Circulation. 2008;118:1183–1194. doi: 10.1161/CIRCULATIONAHA.107.761916. [DOI] [PubMed] [Google Scholar]
  35. Hubbi M. E., Gilkes D. M., Baek J. H., Semenza G. L. J. Biol. Chem. 2012;287:6139–6149. doi: 10.1074/jbc.M111.278630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Karmel P. E., FitzGibbon T. N., Bryan Cave L. California Environmental Law Reporter. 2002;8:226–237. [Google Scholar]
  37. Celli B., MacNee W., Agusti A., Anzueto A., Berg B., Buist A., Calverley P., Chavannes N., Dillard T., Fahy B. Eur. Respir. J. 2004;23:932–946. doi: 10.1183/09031936.04.00014304. [DOI] [PubMed] [Google Scholar]
  38. Eisner M. D., Anthonisen N., Coultas D., Kuenzli N., Perez-Padilla R., Postma D., Romieu I., Silverman E. K., Balmes J. R. Am. J. Respir. Crit. Care Med. 2010;182:693–718. doi: 10.1164/rccm.200811-1757ST. [DOI] [PubMed] [Google Scholar]
  39. Mannino D. M., Buist A. S. Lancet. 2007;370:765–773. doi: 10.1016/S0140-6736(07)61380-4. [DOI] [PubMed] [Google Scholar]
  40. Yoshida M., Sakiyama S., Kenzaki K., Toba H., Uyama K., Takehisa M., Kondo K., Tangoku A. Lab. Invest. 2009;89:760–768. doi: 10.1038/labinvest.2009.34. [DOI] [PubMed] [Google Scholar]
  41. Wu H., Yang S., Wu X., Zhao J., Zhao J., Ning Q., Xu Y., Xie J. Biochem. Biophys. Res. Commun. 2014;450:110–116. doi: 10.1016/j.bbrc.2014.05.073. [DOI] [PubMed] [Google Scholar]
  42. Burrows B., Kettel L. J., Niden A. H., Rabinowitz M., Diener C. F. N. Engl. J. Med. 1972;286:912–918. doi: 10.1056/NEJM197204272861703. [DOI] [PubMed] [Google Scholar]
  43. Weitzenblum E., Hirth C., Ducolone A., Mirhom R., Rasaholinjanahary J., Ehrhart M. Thorax. 1981;36:752–758. doi: 10.1136/thx.36.10.752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Carratu P., Scoditti C., Maniscalco M., Seccia T. M., Di Gioia G., Gadaleta F., Cardone R. A., Dragonieri S., Pierucci P., Spanevello A. BMC Pulm. Med. 2008;8:20. doi: 10.1186/1471-2466-8-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Humbert M., Morrell N. W., Archer S. L., Stenmark K. R., MacLean M. R., Lang I. M., Christman B. W., Weir E. K., Eickelberg O., Voelkel N. F. J. Am. Coll. Cardiol. 2004;43:S13–S24. doi: 10.1016/j.jacc.2004.02.029. [DOI] [PubMed] [Google Scholar]
  46. Calderón-Garcidueñas L., Vincent R., Mora-Tiscareño A., Franco-Lira M., Henríquez-Roldán C., Barragán-Mejía G., Garrido-García L., Camacho-Reyes L., Valencia-Salazar G., Paredes R. Environ. Health Perspect. 2007:1248–1253. doi: 10.1289/ehp.9641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Courtois A., Andujar P., Ladeiro Y., Baudrimont I., Delannoy E., Leblais V., Begueret H., Galland M. A. B., Brochard P., Marano F. Environ. Health Perspect. 2008;116:1294–1299. doi: 10.1289/ehp.11021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Lu T. X., Munitz A., Rothenberg M. E. J. Immunol. 2009;182:4994–5002. doi: 10.4049/jimmunol.0803560. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Mattes J., Collison A., Plank M., Phipps S., Foster P. S. Proc. Natl. Acad. Sci. U. S. A. 2009;106:18704–18709. doi: 10.1073/pnas.0905063106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Mizuno S., Bogaard H. J., Gomez-Arroyo J., Alhussaini A., Kraskauskas D., Cool C. D., Voelkel N. F. Chest. 2012;142:663–672. doi: 10.1378/chest.11-2746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Wu X. B., Wang M. Y., Zhu H. Y., Tang S. Q., You Y. D., Xie Y. Q. Int. J. Clin. Exp. Med. 2014;7:1307–1312. [PMC free article] [PubMed] [Google Scholar]
  52. Feng M. J., Shi F., Qiu C., Peng W. K. Int. Immunopharmacol. 2012;13:347–353. doi: 10.1016/j.intimp.2012.05.001. [DOI] [PubMed] [Google Scholar]
  53. Yang Y., Li X., Yang Q., Wang X., Zhou Y., Jiang T., Ma Q., Wang Y. J. Cancer Genet. Cytogenet. 2010;200:127–133. doi: 10.1016/j.cancergencyto.2010.03.014. [DOI] [PubMed] [Google Scholar]
  54. Crawford M., Batte K., Yu L., Wu X., Nuovo G. J., Marsh C. B., Otterson G. A., Nana-Sinkam S. P. Biochem. Biophys. Res. Commun. 2009;388:483–489. doi: 10.1016/j.bbrc.2009.07.143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Yanaihara N., Caplen N., Bowman E., Seike M., Kumamoto K., Yi M., Stephens R. M., Okamoto A., Yokota J., Tanaka T., Calin G. A., Liu C. G., Croce C. M., Harris C. C. Cancer Cell. 2006;9:189–198. doi: 10.1016/j.ccr.2006.01.025. [DOI] [PubMed] [Google Scholar]
  56. Gao W., Shen H., Liu L., Xu J., Shu Y. J. Cancer Res. Clin. Oncol. 2011;137:557–566. doi: 10.1007/s00432-010-0918-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Jiang H., Zhu Y., Xu H., Sun Y., Li Q. Acta Biochim. Biophys. Sin. 2010;42:483–488. doi: 10.1093/abbs/gmq041. [DOI] [PubMed] [Google Scholar]
  58. Brant K. A., Fabisiak J. P. Am. J. Respir. Cell Mol. Biol. 2013;49:105–113. doi: 10.1165/rcmb.2012-0297OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Weidemann A., Johnson R. Cell Death Differ. 2008;15:621–627. doi: 10.1038/cdd.2008.12. [DOI] [PubMed] [Google Scholar]
  60. Courboulin A., Paulin R., Giguère N. J., Saksouk N., Perreault T., Meloche J., Paquet E. R., Biardel S., Provencher S., Côté J. J. Exp. Med. 2011;208:535–548. doi: 10.1084/jem.20101812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Caruso P., MacLean M. R., Khanin R., McClure J., Soon E., Southgate M., MacDonald R. A., Greig J. A., Robertson K. E., Masson R. Arterioscler., Thromb., Vasc. Biol. 2010;30:716–723. doi: 10.1161/ATVBAHA.109.202028. [DOI] [PubMed] [Google Scholar]
  62. Shimoda L. A., Manalo D. J., Sham J. S., Semenza G. L., Sylvester J. Am. J. Physiol.: Lung Cell. Mol. Physiol. 2001;281:L202–L208. doi: 10.1152/ajplung.2001.281.1.L202. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Click here for additional data file. (161.2KB, pdf)

Articles from Toxicology Research are provided here courtesy of Oxford University Press

RESOURCES