Differential expression of long non-coding RNAs in bleomycin-induced lung fibrosis
- Authors:
- Published online on: June 4, 2013 https://doi.org/10.3892/ijmm.2013.1404
- Pages: 355-364
Abstract
Introduction
Idiopathic pulmonary fibrosis (IPF) is a common, chronic, progressive and usually lethal fibrotic lung disease with poor prognosis (1). This disease is characterized by focal areas of alveolar epithelial cell injury and the excessive proliferation of mesenchymal cells in the interstitium, which results in the excessive deposition of extracellular matrix (ECM) and distorted architecture leading to impaired gas exchange (2,3). Although many pathobiological concepts are emerging, including the role of aging and cellular senescence, oxidative stress, endoplasmic reticulum stress, cellular plasticity, microRNA (miRNA) and mechanotransduction, the molecular mechanisms behind IPF are not yet completely understood (4).
The Encyclopedia of DNA Elements (ENCODE) project, which aimed to comprehensively characterize the human genome, has shown that >90% of the genome has been transcribed; however, only 1–2% of that is composed of genes (5). The majority of these transcripts are not translated into proteins and are, therefore, termed non-coding RNAs (ncRNAs).
Long non-coding RNAs (lncRNAs), a type of ncRNA, vary in size from 200 bp to >100 kb, and are transcribed by RNA polymerase II (6). They play an important role in imprinting (7), enhancing various biological functions (8), X chromosome inactivation (9), chromatin structure (10) and genomic rearrangement during the generation of antibody diversity (11). Thus, lncRNAs are critical for normal development and, in many cases, are deregulated in diseases, such as cancer (12).
Thus far, studies on lncRNAs in IPF are limited. Studies on differentially expressed lncRNAs are necessary for the classification of genes regulated by lncRNAs in IPF. Among the lncRNA profiling technologies, lncRNA microarrays offer a new tool for understanding the biological role of lncRNAs. This study aimed to construct lncRNA expression profiles in bleomycin-induced lung fibrosis and normal lung tissue, in order to identify the differentially expressed lncRNAs and to discover new molecular targets for the therapy of IPF.
Materials and methods
Animals
Sprague Dawley (SD) rats (8 to 12 weeks old) were provided by the Yantai Green Leaf Experimental Animal Center, Yantai, China. A total of 20 SD rats were randomly divided into 2 groups (n=10 in each group): the normal control and lung fibrosis model group. All animal experiments were carried out in accordance with the principles of the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
Bleomycin administration
Rats in the model group were administered 5 mg/kg bleomycin (Daiichi Pure Chemicals Co., Ltd., Tokyo, Japan) dissolved in sterile phosphate-buffered saline (PBS) via a single intratracheal instillation under anesthesia. The normal control rats were administered an equal volume of saline. Lung tissues were harvested on the 28th day following treatment with bleomycin.
Hydroxyproline assay
The total collagen content in the lungs was measured using a colorimetric assay. The lung specimens were washed with normal saline and hydrolysed with 6 ml/l hydrochloric acid at 100ºC for 5 h. The hydrolysates were then diluted with distilled water after neutralizing with sodium hydroxide. The hydroxyproline level in the hydrolysates was then assessed colorimetrically at 560 nm with p-dimethylaminobenzaldehyde, and the results were calculated as mg hydroxyproline/g.
Masson's trichrome
Masson's trichrome method was used to show collagen deposition. After fixing with 4% paraformaldehyde overnight, dehydration in 70% ethanol and clearing in xylene, the lung tissues were embedded in paraffin wax. Sections (4 μm) were prepared and stained with Masson's trichrome. Nine random areas were examined at a magnification of ×400. The severity of fibrosis was blindly determined by a semiquantitative assay.
Observation of cell morphology under a transmission electron microscope (TEM)
Lung tissues were fixed in fresh 3% glutaraldehyde for at least 4 h at 4ºC, post-fixed in 1% osmium tetroxide for 1.5 h, dehydrated in a gradient series of ethanol, infiltrated with Epon 812, embedded and cultured at 37, 45 and 60ºC for 24 h. Ultrathin sections were ultracut using an ultracut E ultramicrotome and stained with uranyl acetate and lead citrate prior to observation under a JEM-1400 TEM (Jeol Ltd., Tokyo, Japan).
RNA isolation and quality assessment
Total RNA from lung tissues was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) and the RNeasy mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. RNA quantity and quality were measured using the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE, USA) and RNA integrity was assessed by standard denaturing agarose gel electrophoresis.
lncRNA microarray
ArrayStar, Inc. (Rockville, MD, USA) rat lncRNA microarray was used and run by the service provider. The rat lncRNA array was designed for profiling the lncRNAs and protein-coding genes. Approximately 9,000 rat lncRNA candidates were identified from the most authoritative databases, such as NCBI RefSeq, UCSC all_mrna records and orthologs of mouse lncRNAs. Highly similar sequences and ncRNAs shorter than 200 bp were excluded. Probes for housekeeping genes and negative probes were printed multiple times to ensure hybridization quality. ArrayStar designed the lncRNA ChIP, taking into account not only the transcript already in the database, but also with the aim of predicting new transcripts. ‘Potential lncRNA’ is a probe designed to help researchers discover new lncRNAs and explore their functions.
RNA labeling and array hybridization
Sample labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technologies, Inc., Santa Clara, CA, USA). Briefly, 1 μg of total RNA from each sample was linearly amplified and labeled with Cy3-dCTP. The labeled cRNAs were purified using the RNeasy mini kit (Qiagen). The concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA) were measured using the NanoDrop ND-1000 spectrophotometer. A total of 1 μg of each labeled cRNA was fragmented by the addition of 11 μl 10X blocking agent and 2.2 μl of 25X fragmentation buffer, then the mixture was heated at 60ºC for 30 min; finally, 55 μl 2X GE hybridization buffer were added to dilute the labeled cRNA. Hybridization solution (100 μl) was dispensed into the gasket slide and assembled to the lncRNA expression microarray slide. The slides were incubated for 17 h at 65ºC in an Agilent hybridization oven. The hybridized arrays were washed, fixed and scanned using the Agilent DNA Microarray Scanner.
Microarray data analysis
Agilent Feature Extraction software (version 10.7.3.1) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies, Inc.). Following quantile normalization of the raw data, lncRNAs and mRNAs with at least 2 out of 2 present or marginal flags were selected for further data analysis. Differentially expressed lncRNAs and mRNAs were identified through fold change filtering. Hierarchical clustering was performed using the Agilent GeneSpring GX software (version 11.5.1). Gene Ontology (GO) analysis and pathway analysis were performed using the standard enrichment computation method.
In situ hybridization
The fixed lung tissues were dehydrated in ethanol, cleared in xylene, transferred to paraffin and sectioned at 5 μm. Paraffin sections were treated with Triton X-100 to enhance probe penetration following conventional dewaxing with water. The slides were washed in PBS and fixed again in 4% paraformaldehyde. After washing in PBS and pre-hybridization for 4 h at 40ºC, the slides were hybridized with digoxin-labeled RNA oligonucleotide probes at 40ºC overnight. The following day, the lung tissue sections were washed with various concentrations of saline sodium citrate (SSC) at 50ºC. After the addition of blocking solution made with sheep serum for 1 h at 37ºC, the slides were incubated with anti-digoxigenin-AP antibody (Roche Diagnostics GmbH, Mannheim, Germany) overnight at 4ºC. Finally, the slides were stained with NBT/BCIP solution (Roche Diagnostics GmbH) avoiding light after being washed with Tris-Nacl buffer. The RNA oligonucleotide probe of AJ005396 was as follows: AATGTCCTTTGGAGGAAGGAGATATGAATTTTATCAATAAATCAAGTCTTGTCTA CCTGG. The RNA oligonucleotide probe of S69206 was as follows: TGCACGAGTCAGAGTCTCCAAGCTAGAGAA CTCTT TTGATATCCCTTGGGATCAACAAG.
Statistical analysis
All data are expressed as the means ± SD. Statistical analysis was performed using SPSS11.0 software by one-way ANOVA. A P-value <0.05 was considered to indicate a statistically significant difference.
Results
Detection of bleomycin-induced lung fibrosis
As the hydroxyproline content can indirectly reflect collagen content, we determined the levels of hydroxyproline to evaluate the degree of fibrosis. The hydroxyproline content in the lungs was significantly increased in the model group compared with the normal group (Fig. 1H). We also used Masson's trichrome staining and TEM to observe histopathological changes and collagen deposition following bleomycin infusion. The results revealed a clear pulmonary structure with an integral air-blood barrier and lower collagen levels in the normal group (Fig. 1A, C and E). By contrast, the structure of the lung tissue was disordered, the air-blood barrier was severely damaged, the pulmonary interalveolar septum was thickened and a significant number of collagen fibers were deposited in the model group (Fig. 1B, D, F and G). These results indicated that we successfully established a model of bleomycin-induced lung fibrosis.
RNA quality control (QC)
The integrity of the RNA was assessed by electrophoresis on a denaturing agarose gel. The RNA run on the denaturing gel had sharp 28S and 18S rRNA bands. The 28S rRNA band was approximately twice as intense as the 18S rRNA band. This 2:1 intensity ratio indicated that the RNA was intact. A NanoDrop ND-1000 spectrophotometer was then used to accurately measure the RNA concentrations (OD260), protein contamination (ratio of OD260/OD280) and organic compound contamination (ratio of OD260/OD230). The OD A260/A280 ratio was close to 2.0. The OD A260/A230 ratio was calculated as >1.8.
Differentially expressed lncRNAs and mRNAs
To examine the potential biological functions of lncRNAs in lung fibrosis, we determined the lncRNA and mRNA expression profiles in bleomycin-induced lung fibrosis through microarray analysis (Fig. 2). Up to 568 lncRNAs were differentially expressed in the bleomycin-treated lung samples compared with the normal control group, among which 210 were upregulated, whereas 358 were downregulated. A total of 945 mRNAs were differentially expressed. Among these mRNAs, 415 were upregulated, whereas 530 were downregulated. The fold change threshold was ≥2.0. The list only shows the partial results for the differentially expressed lncRNAs (Table I) and mRNAs (Table II) in the model vs. the normal control groups.
Location and expression of AJ005396 and S69206
To validate the microarray data and to explore the location of lncRNAs, 2 of the upregulated lncRNAs (AJ005396 and S69206) selected by the fold change and the raw intensities (Table IA) were analyzed by ISH. A blue-violet color indicated a positive reaction. The results revealed that the alveoli have clear hollow cavities, and that the alveolar membranes were thinner in the normal group compared to the model group. AJ005396 and S69206 were observed in the cytoplasm of the interstitial lung cells. Their expression was significantly increased in the model group compared with the normal control group which was consistent with our microarray data (Fig. 3).
Pathway analysis
Pathway analysis was carried out based on the latest Kyoto Encyclopedia of Genes and Genomes (KEGG) database (13). This analysis was used to determine the biological pathways associated with the most differentially expressed mRNAs in lung fibrosis. Up to 22 downregulated and 16 upregulated pathways were identified associated with cytokine-cytokine receptor interaction, chemokine signaling pathway, cell adhesion molecules, Jak/STAT signaling pathway, cell cycle, complement and coagulation cascades, the peroxisome proliferator-activated receptor (PPAR) signaling pathway, as well as others. The predominant pathways are summarized in Fig. 4.
GO analysis
GO analysis is a functional analysis that associates differentially expressed mRNAs. The GO categories were derived from the Gene Ontology website (www.geneontology.org) and comprised of 3 structured networks: biological processes, cellular components and molecular function. According to the GO annotation tool, the differentially expressed genes were principally enriched for GO terms related to immune response, cell differentiation, tyrosine phosphorylation of STAT3 protein linked with biological processes associated with extracellular structure organization, ECM, cytoskeleton, fibrillar collagen involved in cellular components, as well as chemokine activity, chemokine receptor binding, immunoglobulin binding and insulin-like growth factor (IGF) binding in molecular functions. The chart shows the top 10 counts of the significant enrichment terms with the most number of differentially expressed genes (Fig. 5).
Discussion
lncRNAs participate in a wide range of biological processes. Almost every step in the life cycle of genes from transcription to mRNA splicing, RNA decay and translation, is influenced by lncRNAs (14–16). Previous studies have demonstrated that the abnormal expression of lncRNAs contributes to numerous diseases (17–21). However, the profile and the biological function of lncRNAs in lung fibrosis remain unknown. In the present study, we provide new information as to the expression of lncRNAs using a rat model of bleomycin-induced lung fibrosis. To explore the role of lncRNAs in lung fibrosis, a microarray analysis was used to verify the lncRNA expression profiles. Furthermore, we selected 2 lncRNAs, AJ005396 and S69206, to validate the microarray data and explore their location for further research.
lncRNAs can be roughly classified according to the positional relationship of the protein-coding genes in the chromosome. In a recent study, Sui et al divided lncRNAs into 4 groups by analyzing complex transcriptional loci that include lncRNAs and their associated protein-coding genes, namely, cis-antisense lncRNAs, intronic lncRNAs, promoter-associated lncRNAs, and bidirectional lncRNAs (22). According to our microarray results, lncRNAs can also be partially subjected to a similar classification system: MRAK089766, MRAK029230 and MRAK081707, among others, belong to the sense_exon_overlap (the exon of the lncRNA overlaps with a coding transcript exon on the same genomic strand); MRAK045258, MRAK134949 and MRAK080342, among others, represent the sense_intron_overlap (the lncRNA overlaps with the intron of a coding transcript on the same genomic strand); XR_007265, MRuc007wwx and MRAK042040 are categorized under the antisense_exon_overlap (the lncRNA is transcribed from the antisense strand and overlaps with a coding transcript); uc.161+ and XR_007395 comprise the antisense_intron_overlap (the lncRNA is transcribed from the antisense strand without sharing overlapping exons); BC166600 and MRAK042427 are bidirectional (the lncRNA is oriented head to head to a coding transcript within 1,000 bp); and AM293774, Z93366 and XR_007062, among others, are intergenic (there are no coding transcripts within 30 kb of the lncRNA) (Table I).
Although research on lncRNAs has gradually increased, only a few lncRNAs have official names and clear functions. In our microarray results, a vast majority of the differentially expressed lncRNAs in bleomycin-induced lung fibrosis have official names and clear functions, apart from H19, RNA component of mitochondrial RNA processing endoribonuclease (RMRP), and telomerase RNA component (TERC). A previous study demonstrated that H19 regulates biological processes on at least 3 separate levels of the highest significance: in imprinting, as an lncRNA transcript, and as the miR-675 host (23). Another study showed that it can affect the expression of IGF (24). In the present study, H19 was upregulated, whereas IGF binding protein 11 was downregulated. GO analysis showed that some differentially expressed genes were involved in IGF binding. However, whether H19 has a negative regulatory effect on IGF and the regulatory mechanisms involved in IPF require further verification.
RMRP in rats measures 257 nucleotides in length. Maida et al discovered that human telomerase reverse transcriptase (TERT) and RMRP form a distinct ribonucleoprotein complex that has RNA-dependent RNA polymerase activity and produces double-stranded RNAs that can be processed into small interfering RNA in a Dicer (also known as DICER1)-dependent manner (25). TERT mutations have been reported in aplastic anemia and IPF (26). Moreover, the catalytic TERT and TERC are the minimal components of active telomerase (27). TERC not only serves as a template for telomeric DNA synthesis but also plays an important role in catalysis, accumulation, localization and holoenzyme assembly (28–30). Some mutations in TERC can result in a significant change in enzyme activity in vivo and in vitro (30). Recently, Calado et al (31) reported that human telomere disease includes pulmonary fibrosis due to the disruption of the CCAAT box of the TERC promoter, as described by Aalbers et al (32). Accordingly, it can be hypothesized that RMRP and TERC are both crucial to the development process of lung fibrosis.
Two more differentially expressed lncRNAs were detected by in situ hybridization for paraffin-embedded lung tissue: AJ005396 and S69206. To date, limited studies have applied in situ hybridization to measure lncRNA levels in formalin-fixed paraffin-embedded tissue samples. By querying the NCBI database, we found that AJ005396 is an mRNA for collagen α 1 type XI (col11a1) and that S69206 is protease mRNA. In addition, the UCSC database revealed that the AJ005396 and S69206 sequences overlapped with col11a1 and rat mast cell protease 1 precursor (RMCP-1), respectively. In the present study, AJ005396 and S69206 were upregulated, but there was no signficant change in col11a1 and RMCP-1 between the differently expressed mRNAs. Although the database displayed that they contained a potential CDS region, it only predicted protein sequences that must be confirmed with further experiments. Thus, ArrayStar regards them as potential lncRNAs. Moreover, previous studies have shown that parts of lncRNAs overlapped with protein-coding gene sequences, such as MVIH [an lncRNA located within the intron of the ribosomal protein S24 (RPS24) gene] (33) and RERT (an lncRNA whose sequence overlaps with Ras-related GTP-binding protein 4b and EGLN2) (34). Subsequently, they proved using the Open Reading Frame (ORF) Finder and codon substitution frequency analysis that the overexpressed lncRNA, MVIH, in hepatocellular carcinoma (HCC) is a non-coding RNA transcribed independently of the RPS24 gene and that no correlation existed between the transcriptional levels of RPS24 and MVIH (33). Another study showed that the overexpression of the RERT lncRNA upregulated EGLN2 (34). Dharap et al found 62 stroke-responsive lncRNAs showing 90% sequence homology with exons of protein-coding genes in their study on lncRNA expression profiles in focal ischemia (35). Similarly, Ziats et al reported that most differentially expressed lncRNAs in autism spectrum disorders (ASD) were from intergenic regions (~60%), from antisense to protein-coding loci (~15%), or within introns of protein-coding genes (~10%), with the others representing overlapping transcripts from exons or introns in both sense and antisense directions (36). Another complicating factor is the presence of bifunctional RNAs, which are transcripts that function as non-coding transcripts under certain conditions and are translated into functional proteins in other situations (37,38). In view of this, in a subsequent study, we hope to confirm whether AJ005396 and S69206 are lncRNAs and transcribed independently of their overlapping protein-coding RNAs and whether an interaction between their expressions exists.
Furthermore, our array contained probes for known protein-coding transcripts and carried out pathway analysis as well as GO analysis based on differentially expressed mRNAs. Before this, numerous studies of the mRNA or miRNA transcriptome in pulmonary fibrosis have been performed. Research has shown that genes related to the immune system, structural constituents of the cytoskeleton, cellular adhesion, metabolism of the ECM, chemokines and tissue remodeling are overexpressed in fibrotic lesions (39,40), which is consistent with our results. Another study focusing on the KEGG pathway to identify fibrosis-associated genes targeted by miRNAs in the late phase after bleomycin infusion, discovered that they mainly involved cytokine-cytokine receptor interaction, focal adhesion and the Jak/STAT signaling pathway (41). Consistent with this article, cytokine-cytokine receptor interaction exhibited a similarly significant change in our pathway analysis. In addition, the cell cycle, the renin-angiotensin system, the PPAR signaling pathway, the chemokine signaling pathway, cell adhesion molecules, the Jak/STAT signaling pathway and other signaling pathways were involved in the bleomycin-induced lung fibrosis. Recent studies have indicated that PPAR-γ ligands inhibit TGFβ signaling by affecting two pro-survival pathways that culminate in myofibroblast differentiation. Further investigation of PPAR-γ ligands and small electrophilic molecules may lead to a new generation of anti-fibrotic therapeutics (42,43).
In conclusion, the current study used microarray analysis to systematically and comprehensively examine global lncRNA expression in a rat model of bleomycin-induced lung fibrosis and in a normal control group. lncRNA target prediction and further functional characterization may help to elucidate their specific roles in lung injury and fibrosis. The related experiments are currently in progress in our laboratory. Further studies on lncRNA should expand our understanding of the genomic regulatory networks in lung fibrosis and may provide new potential therapeutic targets in lung fibrosis.
Acknowledgements
This study was supported by the ‘Taishan scholar’ position and the National Natural Science Foundation of China (no. 81273957), the Important Project of Science and Technology of Shandong Province (nos. 2010GWZ20254 and 2011GHY11501), the Natural Science Foundation of Shandong Province (nos. ZR2009EM006 and ZR2012HQ042) and the Project of Science and Technology of the Education Department of Shandong Province (no. J11FL87).
Abbreviations:
|
lncRNA |
long non-coding RNA |
|
IPF |
idiopathic pulmonary fibrosis |
|
ECM |
extracellular matrix |
|
TEM |
transmission electron microscope |
|
IGF |
insulin-like growth factor |
|
RMRP |
RNA component of mitochondrial RNA processing endoribonuclease |
|
TERC |
telomerase RNA component |
|
TERT |
telomerase reverse transcriptase |
|
RMCP-1 |
rat mast cell protease 1 precursor |
References
|
American Thoracic Society. Idiopathic pulmonary fibrosis: diagnosis and treatment. International consensus statement American Thoracic Society (ATS), and the European Respiratory Society (ERS). Am J Respir Crit Care Med. 161:646–664. 2000. View Article : Google Scholar | |
|
Noble PW and Homer RJ: Back to the future: historical perspective on the pathogenesis of idiopathic pulmonary fibrosis. Am J Respir Cell Mol Biol. 33:113–120. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Noble PW and Homer RJ: Idiopathic pulmonary fibrosis: new insights into pathogenesis. Clin Chest Med. 25:749–758. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Ding Q, Luckhardt T, Hecker L, et al: New insights into the pathogenesis and treatment of idiopathic pulmonary fibrosis. Drugs. 71:981–1001. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
ENCODE Project Consortium. Birney E, Stamatoyannopoulos JA, Dutta A, et al: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature. 447:799–816. 2007. | |
|
Guttman M, Amit I, Garber M, et al: Chromatin signature reveals over a thousand highly conserved large non-coding RNAs inmammals. Nature. 458:223–227. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Nagano T and Fraser P: Emerging similarities in epigenetic gene silencing by long noncoding RNAs. Mamm Genome. 20:557–562. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Kim A, Zhao H, Ifrim I and Dean A: Beta-globin intergenic transcription and histone acetylation dependent on an enhancer. Mol Cell Biol. 27:2980–2986. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Lee JT: Lessons from X-chromosome inactivation: long ncRNA as guides and tethers to the epigenome. Genes Dev. 23:1831–1842. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Rinn JL, Kertesz M, Wang JK, et al: Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell. 129:1311–1323. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Krangel MS: T cell development: better living through chromatin. Nat Immunol. 8:687–694. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Perez DS, Hoage TR, Pritchett JR, et al: Long, abundantly expressed non-coding transcripts are altered in cancer. Hum Mol Genet. 17:642–655. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Kanehisa M, Goto S, Furumichi M, Tanabe M and Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38:D355–D360. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Mattick JS and Makunin IV: Non-coding RNA. Hum Mol Genet. 15:R17–R29. 2006. View Article : Google Scholar | |
|
Mattick JS: The genetic signatures of noncoding RNAs. PLoS Genet. 5:e10004592009. View Article : Google Scholar : PubMed/NCBI | |
|
Wang KC and Chang HY: Molecular mechanisms of long noncoding RNAs. Mol Cell. 43:904–914. 2011. View Article : Google Scholar | |
|
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS and Manolio TA: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 106:9362–9367. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Lin R, Maeda S, Liu C, Karin M and Edgington TS: A large noncoding RNA is a marker for murine hepatocelluar carcinomas and a spectrum of human carcinomas. Oncogene. 26:851–858. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Qureshi IA, Mattick JS and Mehler MF: Long non-coding RNAs in nervous system function and disease. Brain Res. 1338:20–35. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Tufarelli C, Stanley JA, Garrick D, Sharpe JA, Ayyub H, Wood WG and Higgs DR: Transcription of antisense RNA leading to gene silencing and methylation as a novel cause of human genetic disease. Nat Genet. 34:157–165. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Wapinski O and Chang HY: Long noncoding RNAs and human disease. Trends Cell Biol. 21:354–361. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Sui W, Yan Q, Li H, Liu J, Chen J, Li L and Dai Y: Genome-wide analysis of long noncoding RNA expression in peripheral blood mononuclear cells of uremia patients. J Nephrol. Oct 24–2012.(Epub ahead of print). | |
|
Steck E, Boeuf S, Gabler J, Werth N, Schnatzer P, Diederichs S and Richter W: Regulation of H19 and its encoded microRNA-675 in osteoarthritis and under anabolic and catabolic in vitro conditions. J Mol Med (Berl). 90:1185–1195. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Tran VG, Court F, Duputié A, Antoine E, et al: H19 antisense RNA can up-regulate Igf2 transcription by activation of a novel promoter in mouse myoblasts. PLoS One. 7:e379232012. View Article : Google Scholar : PubMed/NCBI | |
|
Maida Y, Yasukawa M, Furuuchi M, et al: An RNA-dependent RNA polymerase formed by TERT and the RMRP RNA. Nature. 461:230–235. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Calado RT and Young NS: Telomere maintenance and human bone marrow failure. Blood. 111:4446–4455. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Weinrich SL, Pruzan R, Ma L, et al: Reconstitution of human telomerase with the template RNA component hTR and the catalytic protein subunit hTRT. Nat Genet. 17:498–502. 1997. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang Q, Kim NK and Feigon J: Architecture of human telomerase RNA. Proc Natl Acad Sci USA. 108:20325–20332. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Cong Y and Shay JW: Actions of human telomerase beyond telomeres. Cell Res. 18:725–732. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Zvereva MI, Shcherbakova DM and Dontsova OA: Telomerase: structure, functions, and activity regulation. Biochemistry (Mosc). 75:1563–1583. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Calado RT and Young NS: Telomere diseases. N Engl J Med. 361:2353–2365. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Aalbers AM, Kajigaya S, van den Heuvel-Eibrink MM, van der Velden VH, Calado RT and Young NS: Human telomere disease due to disruption of the CCAAT box of the TERC promoter. Blood. 119:3060–3063. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Yuan SX, Yang F, Yang Y, et al: Long noncoding RNA associated with microvascular invasion in hepatocellular carcinoma promotes angiogenesis and serves as a predictor for hepatocellular carcinoma patients' poor recurrence-free survival after hepatectomy. Hepatology. 56:2231–2241. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Zhu Z, Gao X, He Y, et al: An insertion/deletion polymorphism within RERT-lncRNA modulates hepatocellular carcinoma risk. Cancer Res. 72:6163–6172. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Dharap A, Nakka VP and Vemuganti R: Effect of focal ischemia on long noncoding RNAs. Stroke. 43:2800–2802. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Ziats MN and Rennert OM: Aberrant epxpression of long noncoding RNAs in autistic brain. J Mol Neurosci. 49:589–593. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Dinger ME, Pang KC, Mercer TR and Mattick JS: Differentiating protein-coding and noncoding RNA: challenges and ambiguities. PLoS Comput Biol. 4:e10001762008. View Article : Google Scholar : PubMed/NCBI | |
|
Ulveling D, Francastel C and Hube F: When one is better than two: RNA with dual functions. Biochimie. 93:633–644. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Hanaoka M, Ito M, Droma Y, Ushiki A, Kitaguchi Y, Yasuo M and Kubo K: Comparison of gene expression profiling between lung fibrotic and emphysematous tissues sampled from patients with combined pulmonary fibrosis and emphysema. Fibrogenesis Tissue Repair. 5:172012. View Article : Google Scholar : PubMed/NCBI | |
|
Konishi K, Gibson KF, Lindell KO, et al: Gene expression profiles of acute exacerbations of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 180:167–175. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Xie T, Liang J, Guo R, Liu N, Noble PW and Jiang D: Comprehensive microRNA analysis in bleomycin-induced pulmonary fibrosis identifies multiple sites of molecular regulation. Physiol Genomics. 43:479–487. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Kulkarni AA, Thatcher TH, Olsen KC, Maggirwar SB, Phipps RP and Sime PJ: PPAR-γ ligands repress TGFβ-induced myofibroblast differentiation by targeting the PI3K/Akt pathway: implications for therapy of fibrosis. PLoS One. 6:e159092011. | |
|
Ferguson HE, Kulkarni A, Lehmann GM, et al: Electrophilic peroxisome proliferator-activated receptor-γ ligands have potent antifibrotic effects in human lung fibroblasts. Am J Respir Cell Mol Biol. 41:722–730. 2009. |
