Analysis of Long Noncoding RNA Expression Profile in Human Pulmonary Microvascular Endothelial Cells Exposed to Lipopolysaccharide

 

Dong Wang    Changping Gu    Mengjie Liu    Ge Liu    Huan Liu    

Yuelan Wang

 

Department of Anesthesiology, Qianfoshan Hospital, Shandong University, Jinan, China

 

 

 

 

Key Words

Long noncoding RNA • Expression profile • Acute lung injury • Lipopolysaccharide • Human pulmonary microvascular endothelial cell

 

Abstract

Background/Aims: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are a continuum of life-threatening lung changes. Pulmonary vascular injury is one of the most important initial causes of ALI and ARDS. However, the functions of long noncoding RNAs (lncRNAs) in pulmonary endothelial injury remain largely unknown. The aim of the present study was to determine the lncRNA expression profile of human pulmonary microvascular endothelial cells (HPMECs) exposed to lipopolysaccharide (LPS) and explore the potential functions of differentially expressed lncRNAs. Methods: Microarray analysis was used to identify differentially expressed lncRNAs and mRNAs. Bioinformatics analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, lncRNA-mRNA coexpression network and transcription factor (TF)-lncRNA network analyses, were performed to predict the functions of significantly differentially expressed lncRNAs and mRNAs. Real-time polymerase chain reaction (PCR) was used to determine the expression of selected lncRNAs and mRNAs. Results: In this study, we found that 213 lncRNAs and 212 mRNAs were significantly differentially expressed in HPMECs exposed to LPS (fold change > 2.0, p < 0.05). Furthermore, we found that mRNAs co-expressed with lncRNAs were significantly enriched in the TNF signaling pathway, the NF-κB signaling pathway, cell adhesion molecules (CAMs), cytokine-cytokine receptor interactions, and extracellular matrix (ECM)-receptor interactions. The expression levels of all but one of the selected lncRNAs and mRNAs detected by real-time PCR were similar to those detected by microarray analysis. Conclusion: Our data indicate that lncRNAs play an important role in LPS-induced pulmonary endothelial inflammation and barrier dysfunction and may be potential preventive and therapeutic targets for ALI and ARDS.

 

 

Introduction

 

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are part of a continuum of life-threatening lung changes that interfere with the diffusion of oxygen from alveoli into the blood [1-4]. Sepsis and pneumonia are the most common causes of ARDS, and pulmonary vascular injury is one of the most important initial causes of ALI and ARDS. ARDS accounts for 10% of intensive care unit admissions globally. Despite years of basic and clinical studies, the global mortality of ARDS has been as high as 40% in recent years [5, 6]. For these reasons, the identification of key molecules involved in ARDS is highly demanded for improving the clinical outcome of this syndrome.

It is now generally accepted that the majority of mammalian RNA transcripts are noncoding RNAs (ncRNAs). Long noncoding RNAs (lncRNAs), which are longer than 200 nucleotides, are generally not translated into proteins [7-10]. By regulating gene expression at posttranscriptional, transcriptional and epigenetic stages, lncRNAs participate in most essential biological processes. Over the past decade, the study of lncRNAs has become a hot spot in basic and clinical research of ALI and ARDS. Recent studies have shown that miRNAs are involved in the pathophysiology of ARDS and may be interesting diagnostic biomarkers and therapeutic targets [11-20]. However, less is known about the role of lncRNAs in the pathogenesis of ALI and ARDS.

To study the function of lncRNAs in the pathogenesis of ALI and ARDS, we established an experimental model of pulmonary endothelial inflammation and barrier dysfunction by stimulating HPMECs with LPS [21-24] and investigated the expression profile of lncRNAs and mRNAs by microarray analysis. We found that 213 lncRNAs and 212 mRNAs were significantly differentially expressed. Bioinformatics analyses indicated that the differentially expressed lncRNAs might play an important role in LPS-induced pulmonary endothelial inflammation and barrier dysfunction. These findings will serve to increase the understanding of the pathogenesis of pulmonary endothelial dysfunction. Moreover, due to opportunities to identify novel therapeutic and preventive targets, our results may provide relevant information for future clinical interventions of ALI and ARDS.

 

 

Materials and Methods

Cell culture, LPS treatment, and RNA isolation

Human pulmonary microvascular endothelial cells (HPMECs, ScienCell, San Diego, CA, USA) were cultured in endothelial cell medium (ECM, ScienCell, San Diego, CA, USA) in a humidified 5% CO2 incubator at 37°C. LPS (Sigma, St Louis, MO, USA) from Escherichia coli O111:B4 was dissolved in sterile water and prepared fresh at the time of use. At approximately 90% confluence in culture, HPMECs were starved for 1 hour (h) in serum-free medium and then stimulated for 4 h with LPS (1 µg/ml) or vehicle control (PBS) in ECM containing 1% FBS as previously described [14] [21] [25]. HPMECs were used in passage numbers 4 to 6. Total RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The quality of the RNA preparations was verified on an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).

 

Microarray analysis

Total RNA was hybridized to Affymetrix Human Transcriptome Array 2.0 (Affymetrix, Santa Clara, CA, USA). Hybridized data were preprocessed and statistically analyzed as described previously [26, 27]. Differentially expressed lncRNAs and mRNAs were identified by fold-change screening at a threshold of 2.0-fold or greater and a p value < 0.05.

 

GO and KEGG pathway analyses

GO analysis (http://www.geneontology.org) was performed to explore functions of genes based on the biological pathway, cellular component and molecular function categories; Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/) analysis was performed to determine pathways significantly enriched in genes. P < 0.05 and FDR < 0.05 were used as thresholds to define significantly enriched GO terms or pathways.

 

Real-time PCR

A FastQuant RT Kit (Tiangen, Beijing, China) was used to reverse transcribe total RNA into cDNA following the manufacturer's directions. Real-time PCR was performed using SuperReal PreMix Plus (SYBR Green) (Tiangen, Beijing, China) in the Applied Biosystems GeneAmp® PCR System 9700. The reaction conditions were as follows: incubation at 95°C for 15 min, followed by 40 cycles of 95°C for 10 s and 60°C for 20 s. The relative expression levels of lncRNAs were calculated using the 2-ΔΔCt method and normalized to GAPDH levels [28]. The primers for each lncRNA and mRNA are listed in Table 1.

 

Table 1. Primers designed for real-time PCR of candidate lncRNAs and mRNAs. Tm: temperature. bp: base pair

 

LncRNA-mRNA coexpression network

A lncRNA-mRNA coexpression network was constructed to identify the interactions between lncRNA and mRNA according to the normalized signal intensity of specific mRNA and lncRNA expression levels as described previously [29, 30].

 

TF-lncRNA network

Transcription factors (TFs) and DNA sequence motifs from 2.0 kilobase upstream of the transcription start site of differentially expressed lncRNAs were predicted with the TRANSFACT professional database (http://gene-regulation.com/). TFs with a matrix score and core score equal to 1 were selected.

 

Statistical analysis

All data are expressed as the mean ± SEM. For comparisons between 2 groups, unpaired Student's t-test for parametric data and Mann-Whitney's U-test for nonparametric data were used. All statistical analyses were performed with GraphPad Prism 7.04 (GraphPad Software, San Diego, CA, USA). A p value < 0.05 was considered statistically significant.

 

 

Results

 

Profiles of the differentially expressed lncRNAs and mRNAs

To evaluate the differential expression of lncRNAs in HPMECs stimulated with LPS, we performed microarray analysis of the lncRNA expression profile using Affymetrix Human Transcriptome Array 2.0, which covers 245, 349 coding transcripts and 40, 914 noncoding transcripts.

We found that 213 lncRNAs were significantly differentially expressed (fold change > 2.0, p < 0.05). Of these, 189 lncRNAs were upregulated and 28 lncRNAs were downregulated (Fig. 1A, Fig. 1C). The top 20 most significantly upregulated (Table 2) and downregulated (Table 3) lncRNAs are listed below. At the same time, 212 mRNAs were significantly differentially expressed (fold change > 2.0, p < 0.05), including 183 upregulated mRNAs and 29 downregulated mRNAs (Fig. 1B, Fig. 1D). The top 20 most significantly upregulated (Table 4) and downregulated (Table 5) mRNAs are listed below.

Although differentially expressed lncRNAs and mRNAs were widely scattered among all chromosomes, the distribution was not equal (Fig. 1E, Fig. 1F). Chromosome 1 and chromosome 6 had the largest number of differentially expressed mRNAs and lncRNAs, respectively. Chromosome X had the largest number of downregulated lncRNAs. Fifty-two differentially expressed lncRNAs could not be assigned to corresponding chromosomes.

 

Table 2. Top 20 significantly upregulated lncRNAs in HPMECs treated with LPS. Fold: fold change. Chr: chromosome number. LPS 1 to 3 and Ctrl 1 to 3: normalized gene signals of each sample

Table 3. Top 20 significantly downregulated lncRNAs in HPMECs treated with LPS. Fold: fold change. Chr: chromosome number. LPS 1 to 3 and Ctrl 1 to 3: normalized gene signals of each sample

Table 4. Top 20 significantly upregulated mRNAs in HPMECs treated with LPS. Fold: fold change. Chr: chromosome number. LPS 1 to 3 and Ctrl 1 to 3: normalized gene signals of each sample

Table 5. Top 20 significantly downregulated mRNAs in HPMECs treated with LPS. Fold: fold change. Chr: chromosome number. LPS 1 to 3 and Ctrl 1 to 3: normalized gene signals of each sample

Fig. 1. Volcano plots, expression profiles and chromosome distribution of differentially expressed lncRNAs and mRNAs in HPMECs treated with LPS. Volcano plots of differentially expressed lncRNAs (A) and mRNAs (B). Gray dots indicate no change. Blue and red dots indicate significantly downregulated and upregulated lncRNAs and mRNAs, respectively. Hierarchical clustering indicates lncRNA (C) and mRNA (D) profiles. Red and blue columns refer to high and low relative expression, respectively. Distribution of differentially expressed lncRNAs (E) and mRNAs (F).

 

GO and KEGG enrichment analyses

To explore the role of differentially expressed mRNAs in HPMECs treated with LPS, we performed GO and KEGG pathway enrichment analysis.

The results showed that upregulated genes were mainly associated with the following functions: response to lipopolysaccharide (ontology: biological process), integrin complex (ontology: cellular component), and cytokine activity (ontology: molecular function) (Fig. 2A). Downregulated genes were mainly associated with the following functions: male pronucleus (ontology: cellular component), C-terminal protein deglutamylation (ontology: biological process), and exopeptidase activity (ontology: molecular function) (Fig. 2B). The results also indicated that upregulated genes were mainly associated with the following pathways: TNF signaling pathway, cytokine-cytokine receptor interaction, rheumatoid arthritis, NOD-like receptor signaling pathway, and cell adhesion molecules (CAMs) (Fig. 2C). The five most enriched pathways of downregulated genes were mineral absorption, progesterone-mediated oocyte maturation, riboflavin metabolism, cell cycle, and systemic lupus erythematosus (Fig. 2D).

These data suggested that upregulated mRNAs may directly participate in the process of pulmonary endothelial inflammation and barrier dysfunction.

 

Fig. 2. GO and KEGG enrichment analyses for upregulated and downregulated mRNAs. GO enrichment analysis of upregulated (A) and downregulated (B) genes (Top 10, p<0.05). KEGG enrichment analysis for upregulated (C) and downregulated (D) genes (p<0.05). The red to green colors indicate high to low -log (p value) levels. Point size indicates the number of differentially expressed genes in the corresponding pathway.

 

LncRNA-mRNA coexpression networks with GO and KEGG enrichment analysis

To explore the potential biological functions of lncRNAs in HPMECs treated with LPS, we constructed a lncRNA-mRNA coexpression network based on 72 differentially expressed lncRNAs and 132 interacting differentially expressed mRNAs (Fig. 3A). Then, we performed GO and KEGG pathway enrichment analysis on the 132 mRNAs. We found that the most enriched GOs were leukocyte migration (ontology: biological process), cytokine activity (ontology: molecular function), and external side of plasma membrane (ontology: cellular component) (Fig. 3B). The results also indicated that the 132 mRNAs were mainly associated with the following pathways: TNF signaling pathway, NF-κB signaling pathway, CAMs, cytokine-cytokine receptor interaction, and ECM-receptor interaction (Fig. 3C).

 

Fig. 3. Coexpression network of 72 differentially expressed lncRNAs and 132 interacting differentially expressed mRNAs with GO and KEGG analysis results. Coexpression network (A) of 72 lncRNAs and 132 interacting mRNAs. The diamonds represent lncRNAs, and the circles represent their correlated mRNAs. Blue dots and red dots indicate downregulated and upregulated lncRNAs and mRNAs. GO enrichment analysis (B) and KEGG enrichment analysis (C) of the 132 differentially expressed mRNAs (p<0.05).

 

Real-time PCR validation of the microarray data

To validate the reliability of the microarray analysis results and to provide a research basis for further study, the expression levels of 6 lncRNAs (MIR3142HG, n344917, XLOC_l2_015215, n340107, n407205 and XIST) and 8 mRNAs (SELE, IL8, VCAM1, ICAM1, CXCL10, MMP10, ABI3BP and TRPC6) were determined by real-time PCR (Fig. 4). The results showed that MIR3142HG, XLOC_l2_015215, n340107 and n407205 were upregulated, whereas XIST was significantly downregulated. The expression of n344917 showed no significant change. SELE, IL8, VCAM1, ICAM1, CXCL10, MMP10, ABI3BP and TRPC6 were upregulated. These data suggest that the expression levels of selected lncRNAs and mRNAs, except for n344917, detected by real-time PCR were similar to those detected by microarray analysis.

 

Fig. 4. Real-time PCR of six lncRNAs and eight mRNAs in HPMECs. All data are means ± SEM (n=3), Student's t-test. P<0.05; ∗∗ P<0.01; ∗∗∗ P<0.001; NS, not significant.

 

TF-lncRNA regulatory network

To understand the reason for the differential expression of the five validated lncRNAs (MIR3142HG, XLOC_l2_015215, n340107, n407205 and XIST), we predicted TFs mapping to these lncRNAs in the TRANSFACT professional database. The results showed that of the 214 TFs mapping to these lncRNAs (Fig. 5), 17 out of the 214 TFs were differently expressed (fold change > 1.2, p < 0.05). Seven differentially expressed TFs were predicted to regulate the transcription of XIST (Table 6), including 2 upregulated TFs (POU2F2 and BCL6) and 5 downregulated mRNAs (SOX18, MEF2C, SOX17, HOXB5 and ETS2). These findings indicate that differently expressed TFs might be one of the causes of the differential expression of lncRNAs.

 

Fig. 5. TF regulatory network of the five validated lncRNAs. The diamonds represent lncRNAs. Blue and red dots indicate downregulated and upregulated lncRNAs. Yellowish triangles represent correlated TFs.

Table 6. Differentially expressed TFs predicted to regulate the transcription of XIST. Range: the promoter region of XIST. Position: the position of the transcription start site. Sequence: the TF binding sequences in the promoter region

 

 

Discussion

 

In this study, we established an experimental model of pulmonary endothelial inflammation and barrier dysfunction by stimulating HPMECs with LPS and investigated the expression profile of lncRNAs and mRNAs by microarray analysis. The results indicate that lncRNAs play an important role in LPS-induced pulmonary endothelial inflammation and barrier dysfunction.

An overwhelming majority of the transcriptions of the human genome are ncRNAs, which act as important transcriptional regulators in pathophysiologic processes [31-33]. In contrast to miRNAs, which regulate target genes by a posttranscriptional mechanism [34, 35], lncRNAs have the potential to regulate gene expression at posttranscriptional, transcriptional and epigenetic levels [36-39]. Furthermore, many lncRNAs have shown developmental stage-specific and tissue-specific expression patterns [40, 41].

The roles of ncRNAs in the pathogenesis of ALI and ARDS have attracted increasing attention. Recent studies have demonstrated that several miRNAs, such as miR-146, miR-155, and miR-221, serve as important regulators of inflammation-related mediators [33]. Hongbin Li et al. reported that the lncRNA CASC2 improved ALI by reducing lung epithelial cell apoptosis [42]. However, the function of lncRNA has not been investigated in pulmonary vascular injury associated with ALI and ARDS. Unlike two previous studies that analyzed the lncRNA expression profile in human umbilical vein endothelial cells [43] and human dermal microvascular endothelial cells [44] exposed to LPS, we identified the expression of LPS-responsive lncRNAs in HPMECs to investigate the role of lncRNAs in pulmonary endothelial injury.

In the present study, we determined the expression of 6 lncRNAs and 8 mRNAs by real-time PCR. The selection of these lncRNAs and mRNAs was based on the fold change and degree data in the lncRNA-mRNA coexpression network. Our results showed that the lncRNA and mRNA expression results determined by microarray analysis had good reliability and reproducibility. LPS successfully activated HPMECs because the expression of SELE, IL-8, VCAM-1, CCL20 and ICAM-1 increased significantly in the experimental model [45-47]. In addition, GO and KEGG pathway enrichment analyses also suggested that the differentially expressed mRNAs participated in pulmonary endothelial inflammation and barrier dysfunction. Notably, based on GO and KEGG pathway enrichment analyses of the lncRNA-mRNA coexpression network, we found that 72 differentially expressed lncRNAs might be mainly involved in the TNF signaling pathway, NF-κB signaling pathway, CAMs, cytokine-cytokine receptor interactions, and ECM-receptor interactions [48, 49]. Thus, we speculated that these lncRNAs might be associated with pulmonary endothelial inflammation and barrier dysfunction. Furthermore, we predicted differentially expressed TFs mapping to five selected lncRNAs that had been verified by real-time PCR with the help of the TRANSFAC professional database. The results suggested that differently expressed TFs might be one of the causes of the differential expression of lncRNAs.

The understanding of the diversity of gene regulation has greatly expanded in the past decade. There is increasing recognition that ncRNAs are important components of the gene regulatory network [50]. As the roles of lncRNAs become clearer, the knowledge acquired by this research will enable the understanding of how lncRNAs affect the initiation, progression, and resolution of pulmonary endothelial dysfunction associated with ALI and ARDS. Our data still need to be further validated in both vitro and vivo.

XIST, which is the master regulator of X chromosome inactivation, has been reported to play an important role in the pathogenesis of many diseases [51, 52]. H Yu et al. reported that XIST inhibition increased the blood-tumor barrier permeability in glioma endothelial cells [53]. Here, we found that XIST was significantly downregulated in HPMECs exposed to LPS by both microarray data and real-time PCR. This is the first study to determine the expression of XIST in HPMECs. The results may be helpful for further insights into the underlying role and mechanism of XIST in pulmonary endothelial inflammation and barrier dysfunction. The detailed function of XIST in pulmonary endothelial injury still needs to be further investigated.

Although not a genetic disease, ARDS has a certain hereditary susceptibility [54]. Three retrospective studies reported that men were at higher risk than women of incidence and mortality from ARDS [55-57]. In this study, we found that the distribution of differentially expressed lncRNAs was not equal. Interestingly, most differentially expressed lncRNAs on chromosome X were downregulated, while all differentially expressed lncRNAs on chromosome Y were upregulated. We speculate that the differential expression of lncRNAs on chromosomes X and Y may be one of the causes of sex-related differences in the morbidity and mortality of ARDS. Of course, this subject still requires more clinical research and further investigations.

 

Conclusion

 

In conclusion, we investigated the expression of lncRNAs and mRNAs in HPMECs treated with LPS by microarray analysis. We found that lncRNAs may be involved in LPS-induced pulmonary endothelial inflammation and barrier dysfunction. This is the first study to reveal the expression profile and potential role of lncRNAs in HPMECs. These findings provide a novel direction for both basic and clinical research for ALI and ARDS. Due to opportunities to identify novel therapeutic and preventive targets, our results may provide relevant information for future clinical interventions of ALI and ARDS.

 

Acknowledgements

 

This work was supported by the National Natural Science Foundation of China (No. 81770076, No. 81570074 and No. 81600054) and Joint Project of Shandong Natural Science Foundation (ZR2015HL002).

 

Disclosure Statement

 

The authors have no conflicts of interest to declare.

 

 

References

 

1 Matthay MA, Zemans RL: The acute respiratory distress syndrome: pathogenesis and treatment. Annu Rev Pathol 2011;6:147-163.

https://doi.org/10.1146/annurev-pathol-011110-130158

PMID: 20936936 PMCid:PMC3108259

 

2 Matthay MA, Ware LB, Zimmerman GA: The acute respiratory distress syndrome. J Clin Invest 2012;122:2731-2740.

https://doi.org/10.1172/JCI60331

PMID: 22850883 PMCid:PMC3408735

 

3 Lee KY: Pneumonia, Acute Respiratory Distress Syndrome, and Early Immune-Modulator Therapy. Int J Mol Sci 2017;18:pii:E388.

https://doi.org/10.3390/ijms18020388

PMID:28208675

 

4 Yang CY, Chen CS, Yiang GT, Cheng YL, Yong SB, Wu MY, Li CJ: New Insights into the Immune Molecular Regulation of the Pathogenesis of Acute Respiratory Distress Syndrome. Int J Mol Sci 2018;19:pii:E588.

https://doi.org/10.3390/ijms19020588

PMID:29462936

 

5 Fan E, Brodie D, Slutsky AS: Acute Respiratory Distress Syndrome: Advances in Diagnosis and Treatment. JAMA 2018;319:698-710.

https://doi.org/10.1001/jama.2017.21907

PMID: 29466596

 

6 Wohlrab P, Kraft F, Tretter V, Ullrich R, Markstaller K, Klein KU: Recent advances in understanding acute respiratory distress syndrome. F1000Res 2018; DOI:10.12688/f1000research.

https://doi.org/10.12688/f1000research.11148.1

Pmid:29568488

 

7 Quinn JJ, Chang HY: Unique features of long non-coding RNA biogenesis and function. Nat Rev Genet 2016;17:47-62.

https://doi.org/10.1038/nrg.2015.10

PMID: 26666209

 

8 Batista PJ, Chang HY: Long noncoding RNAs: Cellular address codes in development and disease. Cell 2013;152:1298-1307.

https://doi.org/10.1016/j.cell.2013.02.012

PMID: 23498938 PMCid:PMC3651923

 

9 Rinn JL, Chang HY: Genome regulation by long noncoding RNAs. Annu Rev Biochem 2012;81:145-166.

https://doi.org/10.1146/annurev-biochem-051410-092902

PMID: 22663078 PMCid:PMC3858397

 

10 Ponting CP, Oliver PL, Reik W: Evolution and functions of long noncoding RNAs. Cell 2009;136:629-641.

https://doi.org/10.1016/j.cell.2009.02.006

PMID: 19239885

 

11 Tao Z, Yuan Y, Liao Q: Alleviation of Lipopolysaccharides-Induced Acute Lung Injury by miR-454. Cell Physiol Biochem 2016;38:65-74.

https://doi.org/10.1159/000438609

PMID: 26741509

 

12 Cai ZG, Zhang SM, Zhang Y, Zhou YY, Wu HB, Xu XP: MicroRNAs are dynamically regulated and play an important role in LPS-induced lung injury. Can J Physiol Pharmacol 2012;90:37-43.

https://doi.org/10.1139/y11-095

PMID: 22185353

 

13 Li W, Qiu X, Jiang H, Han Y, Wei D, Liu J: Downregulation of miR-181a protects mice from LPS-induced acute lung injury by targeting Bcl-2. Biomed Pharmacother 2016;84:1375-1382.

https://doi.org/10.1016/j.biopha.2016.10.065

PMID: 27802900

 

14 Fang Y, Gao F, Hao J, Liu Z: microRNA-1246 mediates lipopolysaccharide-induced pulmonary endothelial cell apoptosis and acute lung injury by targeting angiotensin-converting enzyme 2. Am J Transl Res 2017;9:1287-1296.

PMID: 28386354 PMCid:PMC5376019

 

15 Xie T, Liang J, Liu N, Wang Q, Li Y, Noble PW, Jiang D: MicroRNA-127 inhibits lung inflammation by targeting IgG Fcgamma receptor I. J Immunol 2012;188:2437-2444.

https://doi.org/10.4049/jimmunol.1101070

PMID: 22287715 PMCid:PMC3288289

 

16 Ying H, Kang Y, Zhang H, Zhao D, Xia J, Lu Z, Wang H, Xu F, Shi L: MiR-127 modulates macrophage polarization and promotes lung inflammation and injury by activating the JNK pathway. J Immunol 2015;194:1239-1251.

https://doi.org/10.4049/jimmunol.1402088

Mid:25520401

 

17 Davidson-Moncada J, Papavasiliou FN, Tam W: MicroRNAs of the immune system: roles in inflammation and cancer. Ann N Y Acad Sci 2010;1183:183-194.

https://doi.org/10.1111/j.1749-6632.2009.05121.x

PMID: 20146715 PMCid:PMC2876712

 

18 Vergadi E, Vaporidi K, Theodorakis EE, Doxaki C, Lagoudaki E, Ieronymaki E, Alexaki VI, Helms M, Kondili E, Soennichsen B, Stathopoulos EN, Margioris AN, Georgopoulos D, Tsatsanis C: Akt2 deficiency protects from acute lung injury via alternative macrophage activation and miR-146a induction in mice. J Immunol 2014;192:394-406.

https://doi.org/10.4049/jimmunol.1300959

PMID: 24277697

 

19 Zeng Z, Gong H, Li Y, Jie K, Ding C, Shao Q, Liu F, Zhan Y, Nie C, Zhu W, Qian K: Upregulation of miR-146a contributes to the suppression of inflammatory responses in LPS-induced acute lung injury. Exp Lung Res 2013;39:275-282.

https://doi.org/10.3109/01902148.2013.808285

PMID: 23848342

 

20 Han Y, Li Y, Jiang Y: The Prognostic Value of Plasma MicroRNA-155 and MicroRNA-146a Level in Severe Sepsis and Sepsis-Induced Acute Lung Injury Patients. Clin Lab 2016;62:2355-2360.

https://doi.org/10.7754/Clin.Lab.2016.160511

PMID: 28164567

 

21 Li H, He B, Liu X, Li J, Liu Q, Dong W, Xu Z, Qian G, Zuo H, Hu C, Qian H, Mao C, Wang G: Regulation on Toll-like Receptor 4 and Cell Barrier Function by Rab26 siRNA-loaded DNA Nanovector in Pulmonary Microvascular Endothelial Cells. Theranostics 2017;7:2537-2554.

https://doi.org/10.7150/thno.17584

PMID: 28744333 PMCid:PMC5525755

 

22 Nickols J, Obiako B, Ramila KC, Putinta K, Schilling S, Sayner SL: Lipopolysaccharide-induced pulmonary endothelial barrier disruption and lung edema: critical role for bicarbonate stimulation of AC10. Am J Physiol Lung Cell Mol Physiol 2015;309:L1430-1437.

https://doi.org/10.1152/ajplung.00067.2015

PMID: 26475732 PMCid:PMC4683319

 

23 Menden H, Tate E, Hogg N, Sampath V: LPS-mediated endothelial activation in pulmonary endothelial cells: role of Nox2-dependent IKK-beta phosphorylation. Am J Physiol Lung Cell Mol Physiol 2013;304:L445-455.

https://doi.org/10.1152/ajplung.00261.2012

PMID: 23333803 PMCid:PMC3602745

 

24 Li L, Hu J, He T, Zhang Q, Yang X, Lan X, Zhang D, Mei H, Chen B, Huang Y: P38/MAPK contributes to endothelial barrier dysfunction via MAP4 phosphorylation-dependent microtubule disassembly in inflammation-induced acute lung injury. Sci Rep 2015;5:8895.

https://doi.org/10.1038/srep08895

PMID: 25746230 PMCid:PMC4352893

 

25 Dreymueller D, Martin C, Kogel T, Pruessmeyer J, Hess FM, Horiuchi K, Uhlig S, Ludwig A: Lung endothelial ADAM17 regulates the acute inflammatory response to lipopolysaccharide. EMBO Mol Med 2012;4:412-423.

https://doi.org/10.1002/emmm.201200217

PMID: 22367719 PMCid:PMC3403298

 

26 Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003;31:e15.

https://doi.org/10.1093/nar/gng015

PMID: 12582260 PMCid:PMC150247

 

27 Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:Article3.

https://doi.org/10.2202/1544-6115.1027

PMID: 16646809

 

28 Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001;25:402-408.

https://doi.org/10.1006/meth.2001.1262

PMID: 11846609

 

29 Pujana MA, Han JD, Starita LM, Stevens KN, Tewari M, Ahn JS, Rennert G, Moreno V, Kirchhoff T, Gold B, Assmann V, Elshamy WM, Rual JF, Levine D, Rozek LS, Gelman RS, Gunsalus KC, Greenberg RA, Sobhian B, Bertin N, et al.: Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet 2007;39:1338-1349.

https://doi.org/10.1038/ng.2007.2

PMID: 17922014

 

30 Prieto C, Risueno A, Fontanillo C, De las Rivas J: Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One 2008;3:e3911.

https://doi.org/10.1371/journal.pone.0003911

PMID: 19081792 PMCid:PMC2597745

 

31 Hackermuller J, Reiche K, Otto C, Hosler N, Blumert C, Brocke-Heidrich K, Bohlig L, Nitsche A, Kasack K, Ahnert P, Krupp W, Engeland K, Stadler PF, Horn F: Cell cycle, oncogenic and tumor suppressor pathways regulate numerous long and macro non-protein-coding RNAs. Genome Biol 2014;15:R48.

https://doi.org/10.1186/gb-2014-15-3-r48

PMID: 24594072 PMCid:PMC4054595

 

32 Cardinal-Fernandez P, Ferruelo A, Esteban A, Lorente JA: Characteristics of microRNAs and their potential relevance for the diagnosis and therapy of the acute respiratory distress syndrome: from bench to bedside. Transl Res 2016;169:102-111.

https://doi.org/10.1016/j.trsl.2015.11.004

PMID: 26687392

 

33 Marques-Rocha JL, Samblas M, Milagro FI, Bressan J, Martinez JA, Marti A: Noncoding RNAs, cytokines, and inflammation-related diseases. FASEB J 2015;29:3595-3611.

https://doi.org/10.1096/fj.14-260323

PMID: 26065857

 

34 Rebane A, Akdis CA: MicroRNAs: Essential players in the regulation of inflammation. J Allergy Clin Immunol 2013;132:15-26.

https://doi.org/10.1016/j.jaci.2013.04.011

PMID: 23726263

 

35 Djuranovic S, Nahvi A, Green R: A parsimonious model for gene regulation by miRNAs. Science 2011;331:550-553.

https://doi.org/10.1126/science.1191138

PMID: 21292970 PMCid:PMC3955125

 

36 Prensner JR, Chinnaiyan AM: The emergence of lncRNAs in cancer biology. Cancer Discov 2011;1:391-407.

https://doi.org/10.1158/2159-8290.CD-11-0209

PMID: 22096659 PMCid:PMC3215093

 

37 Villegas VE, Zaphiropoulos PG: Neighboring gene regulation by antisense long non-coding RNAs. Int J Mol Sci 2015;16:3251-3266.

https://doi.org/10.3390/ijms16023251

PMID: 25654223 PMCid:PMC4346893

 

38 Karapetyan AR, Buiting C, Kuiper RA, Coolen MW: Regulatory Roles for Long ncRNA and mRNA. Cancers (Basel) 2013;5:462-490.

https://doi.org/10.3390/cancers5020462

Mid:24216986 PMCid:PMC3730338

 

39 Angrand PO, Vennin C, Le Bourhis X, Adriaenssens E: The role of long non-coding RNAs in genome formatting and expression. Front Genet 2015;6:165.

https://doi.org/10.3389/fgene.2015.00165

PMID: 25972893 PMCid:PMC4413816

 

40 Jehan Z, Vallinayagam S, Tiwari S, Pradhan S, Singh L, Suresh A, Reddy HM, Ahuja YR, Jesudasan RA: Novel noncoding RNA from human Y distal heterochromatic block (Yq12) generates testis-specific chimeric CDC2L2. Genome Res 2007;17:433-440.

https://doi.org/10.1101/gr.5155706

PMID: 17095710 PMCid:PMC1832090

 

41 Kutter C, Watt S, Stefflova K, Wilson MD, Goncalves A, Ponting CP, Odom DT, Marques AC: Rapid turnover of long noncoding RNAs and the evolution of gene expression. PLoS Genet 2012;8:e1002841.

https://doi.org/10.1371/journal.pgen.1002841

PMID: 22844254 PMCid:PMC3406015

 

42 Li H, Shi H, Gao M, Ma N, Sun R: Long non-coding RNA CASC2 improved acute lung injury by regulating miR-144-3p/AQP1 axis to reduce lung epithelial cell apoptosis. Cell Biosci 2018;8:15.

https://doi.org/10.1186/s13578-018-0205-7

PMID: 29492259 PMCid:PMC5828141

 

43 Singh KK, Matkar PN, Muhammad S, Quan A, Gupta V, Teoh H, Al-Omran M, Verma S: Investigation of novel LPS-induced differentially expressed long non-coding RNAs in endothelial cells. Mol Cell Biochem 2016;421:157-168.

https://doi.org/10.1007/s11010-016-2797-8

PMID: 27565812

 

44 Chowdhury IH, Narra HP, Sahni A, Khanipov K, Schroeder CLC, Patel J, Fofanov Y, Sahni SK: Expression Profiling of Long Noncoding RNA Splice Variants in Human Microvascular Endothelial Cells: Lipopolysaccharide Effects In vitro. Mediators Inflamm 2017;2017:3427461.

https://doi.org/10.1155/2017/3427461

PMID: 29147069

 

45 Liu G, Place AT, Chen Z, Brovkovych VM, Vogel SM, Muller WA, Skidgel RA, Malik AB, Minshall RD: ICAM-1-activated Src and eNOS signaling increase endothelial cell surface PECAM-1 adhesivity and neutrophil transmigration. Blood 2012;120:1942-1952.

https://doi.org/10.1182/blood-2011-12-397430

PMID: 22806890 PMCid:PMC3433096

 

46 Pober JS, Sessa WC: Evolving functions of endothelial cells in inflammation. Nat Rev Immunol 2007;7:803-815.

https://doi.org/10.1038/nri2171

PMID: 17893694

 

47 Zhou M, Ding WJ, Chen YW, Shen F, Zeng JY, Qu CY, Wei YF, Xu LM: Expression Changes of Long Noncoding RNA in the Process of Endothelial Cell Activation. Cell Physiol Biochem 2017;41:115-123.

https://doi.org/10.1159/000455980

PMID: 28114126

 

48 van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG: The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol 2017;17:407-420.

https://doi.org/10.1038/nri.2017.36

PMID: 28436424

 

49 Danese S, Dejana E, Fiocchi C: Immune regulation by microvascular endothelial cells: directing innate and adaptive immunity, coagulation, and inflammation. J Immunol 2007;178:6017-6022.

https://doi.org/10.4049/jimmunol.178.10.6017

PMID: 17475823

 

50 Wang KC, Chang HY: Molecular mechanisms of long noncoding RNAs. Mol Cell 2011;43:904-914.

https://doi.org/10.1016/j.molcel.2011.08.018

PMID: 21925379 PMCid:PMC3199020

 

51 Pintacuda G, Young AN, Cerase A: Function by Structure: Spotlights on Xist Long Non-coding RNA. Front Mol Biosci 2017;4:90.

https://doi.org/10.3389/fmolb.2017.00090

PMID: 29302591 PMCid:PMC5742192

 

52 Xiong Y, Wang L, Li Y, Chen M, He W, Qi L: The Long Non-Coding RNA XIST Interacted with MiR-124 to Modulate Bladder Cancer Growth, Invasion and Migration by Targeting Androgen Receptor (AR). Cell Physiol Biochem 2017;43:405-418.

https://doi.org/10.1159/000480419

PMID: 28869948

 

53 Yu H, Xue Y, Wang P, Liu X, Ma J, Zheng J, Li Z, Li Z, Cai H, Liu Y: Knockdown of long non-coding RNA XIST increases blood-tumor barrier permeability and inhibits glioma angiogenesis by targeting miR-137. Oncogenesis 2017;6:e303.

https://doi.org/10.1038/oncsis.2017.7

PMID: 28287613 PMCid:PMC5533948

 

54 Gao L, Barnes KC: Recent advances in genetic predisposition to clinical acute lung injury. Am J Physiol Lung Cell Mol Physiol 2009;296:L713-725.

https://doi.org/10.1152/ajplung.90269.2008

PMID: 19218355 PMCid:PMC2681344

 

55 Dodoo-Schittko F, Brandstetter S, Brandl M, Blecha S, Quintel M, Weber-Carstens S, Kluge S, Meybohm P, Rolfes C, Ellger B, Bach F, Welte T, Muders T, Thomann-Hackner K, Bein T, Apfelbacher C: Characteristics and provision of care of patients with the acute respiratory distress syndrome: descriptive findings from the DACAPO cohort baseline and comparison with international findings. J Thorac Dis 2017;9:818-830.

https://doi.org/10.21037/jtd.2017.03.120

PMID: 28449491 PMCid:PMC5394069

 

56 Dodoo-Schittko F, Brandstetter S, Brandl M, Blecha S, Quintel M, Weber-Carstens S, Kluge S, Kirschning T, Muders T, Bercker S, Ellger B, Arndt C, Meybohm P, Adamzik M, Goldmann A, Karagiannidis C, Bein T, Apfelbacher C, DACAPO Study Group: German-wide prospective DACAPO cohort of survivors of the acute respiratory distress syndrome (ARDS): a cohort profile. BMJ Open 2018;8:e019342.

https://doi.org/10.1136/bmjopen-2017-019342

PMID: 29622574 PMCid:PMC5892755

 

57 Cochi SE, Kempker JA, Annangi S, Kramer MR, Martin GS: Mortality Trends of Acute Respiratory Distress Syndrome in the United States from 1999 to 2013. Ann Am Thorac Soc 2016;13:1742-1751.

https://doi.org/10.1513/AnnalsATS.201512-841OC

PMID: 27403914