Microarray analysis of long non-coding RNAs in COPD lung tissue
Hui Bi1§, Ji Zhou1§, Dandan Wu1, Wei Gao1, Lingling Li1, Like Yu2, Feng Liu2, Mao Huang1, Ian M
Adcock3, Peter J Barnes3, Xin Yao1*
1Department
of Respiratory Medicine, The First Affiliated Hospital of Nanjing Medical University,
Nanjing, China; 2 Department of Respiratory Medicine, Nanjing Chest Hospital; 3Airway Disease Section,
National Heart and Lung Institute, Imperial College, London, UK
§These authors contributed equally to this work.
*Corresponding author
Dr Xin Yao
Department of Respiratory Medicine, The First Affiliated Hospital of Nanjing Medical University, 300
Guangzhou Road, Nanjing, China, 210029
Tel: (86) 18651608881; Fax: (86) 2583724440
Email: yaoxin@njmu.edu.cn
Keywords: long noncoding RNA, COPD, Microarray analysis
Abstract
Background: Long noncoding RNAs (lncRNAs) play an important regulatory role in a
variety of biological and pathological processes and are implicated in the pathogenesis of
many human diseases.
In this study, we describe the expression of lncRNAs and mRNAs in
lung tissue form three non-smokers, five smokers without chronic obstructive pulmonary
disease (COPD) and five smokers with COPD using microarray analysis.
Methodology: RNA was extracted from human lung tissue and analysed using an Agilent
Human lncRNA + mRNA Array v2.0 system.
Gene Ontology (GO) and pathway analysis
was then performed and mapped genes to the Kyoto Encyclopedia of Genes and Genomes
(KEGG) database.
Results: 39,253 distinct lncRNA transcripts were detected in the lung tissues of all subjects.
In smokers without COPD 87 lncRNAs were significantly up-regulated and 244
down-regulated compared to non-smokers with RNA50010︱UCSC-9199-1005 (fold-change:
13.02297) and RNA58351|CombinedLit_316_550 (fold-change: 12.609704) the most overand under-regulated respectively.
In contrast, in COPD patients 120 lncRNAs were
overexpressed and 43 underexpressed compared with smokers without COPD with
RNA44121 ︱ UCSC-2000-3182
(fold-change:
8.721127)
and
RNA43510 ︱
UCSC-1260-3754 (fold-change: 5.527549) being the most over- and under-regulated
respectively.
GO and pathway analysis indicated that cigarette smoking was associated with
activation of metabolic pathways and whereas COPD transcripts were associated with
‘hematopoietic cell lineage', intermediary metabolism and immune system processes.
Conclusions/Significance: Our study is the first to determine the genome-wide expression of
lncRNAs in lung tissue by microarray analysis.
The results show that cigarette smoking and
the presence of COPD alters the expression of lncRNAs in lung tissues and that these
lncRNAs might play a roles in modulating pathways implicated in disease onset and
progression such as intermediary metabolism and the immune system.
Introduction
Chronic obstructive pulmonary disease (COPD), a progressive disease characterised by
airway limitation, is considered the third leading cause of mortality today and estimated by
researchers to become the sixth most common cause of disability world-wide by 2020.1
COPD seriously threatens the quality of life of humans, and treatment of the disease often
results in high medical costs.2,3
To the best of our knowledge, inflammation,
protease-antiprotease imbalance and oxidative stress play crucial roles in the progression of
COPD but they are not fully accountable for the disease process.
Although cigarette
smoking is the most common risk factor that contributes to the pathogenesis of COPD, only
about 20% of smokers develop COPD,4 which suggests that genetic predisposition or
epigenetic factors or some other cellular susceptibility mechanisms must play an important
role in determining which smokers develop
airway obstruction.
With the advancement of high-resolution microarray analysis methods and genome-wide
sequencing technology, long noncoding RNA (lncRNA) has generated considerable interest
over the last decade.
LncRNA is generally defined as transcripts of greater than 200
nucleotides without protein coding functions.
LncRNAs play important roles in the
regulation of gene expression5-7 in diverse biological and pathological processes8-14 and are
abnormally expressed in many human diseases.14
Several studies show that lncRNAs
participate in the process of oxidative stress, inflammation, apoptosis, cell growth and
viability.13,15,16
Thai et al.,16 for example, recently found that lncRNA-SCAL1 (smoke and
cancer-associated lncRNA1) could mediate protection against oxidative stress-induced by
cigarette smoke extract by acting downstream of NRF2 in airway epithelial cells.
Over the
last decade, although there has been considerable progress in understanding lncRNAs;, the
exact functions of most lncRNAs remain unknown.
The expression and biological functions
of lncRNAs in COPD are currently unknown.
In our study, we profiled the expression of lncRNAs and mRNAs in the lung tissue of
three non-smokers, five smokers without COPD and five smokers with COPD using
microarray analysis.
Our results showed that lncRNA and mRNA expression profiles differ
significantly between the lung tissues of non-smokers, smokers and smokers with COPD.
This finding suggests that altered expression levels of lncRNAs play important roles in the
occurrence and development of COPD.
An investigation into the relations between various
signal pathways and lncRNAs may supply novel insights into the molecular regulation of
COPD and provide new methods for diagnosing and treating COPD.
Materials and Methods
Patient samples and RNA extraction
The study was approved by the Medical Ethical Committee of the First Affiliated Hospital of
Nanjing Medical University. Samples were obtained from lung resection tissue taken from
three non-smokers, five normal smokers and five smokers with COPD for microarray
analysis of lncRNAs. Smokers and non-smokers were on no medication, whilst 3 COPD
patients were on inhaled corticosteroids andβ-adrenergic agonists and three only used
theophylline. All patients were confirmed as suffering from lung cancer following
pathological analysis. Clinical information on these subjects is summarised in Table 1.
Lung tissue extracted from each subject was snap-frozen in liquid nitrogen immediately after
resection. Total RNA was extracted using Trizol reagent (Invitrogen) according to the
manufacturer’s instructions. Total RNA from each sample was quantified using a NanoDrop
ND-1000, and RNA integrity was assessed using standard denaturing agarose gel
electrophoresis.
DNA microarray
An Agilent Human lncRNA + mRNA Array v2.0 system was designed with four identical
arrays per slide (4 x 180K format); each array contained a probe set comprising 39,000
human lncRNA transcripts and 32,000 human mRNAs.
These lncRNA and mRNA target
sequences were merged from multiple databases: 4,765 were from RefSeq; 12,754 were from
ENSEMBL; 8,195 lincRNA were from the John Rinn Lab;17 1,289 were from NRED
(ncRNA Expression Database); 17,203 were from H-InvDB; 2,975 were from ENCODE; 529
were from Combined Lit; 1,053 were from Antisense ncRNA pipeline; 407 were from Hox
ncRNAs; 481 were from UCRs and 848 were from the Chen Ruisheng Lab (Institute of
Biophysics, Chinese Academy of Science).
Each RNA transcript was detected by 2 probes.
The array also contained 4,974 Agilent control probes.
RNA amplification, labelling and hybridisation
cDNA labelled with fluorescent dyes (Cy5 and Cy3-dCTP) was produced using Eberwine’s
linear RNA amplification method with subsequent enzymatic reaction.18
The yield of
labelled cDNA was improved by using a CapitalBio cRNA amplification and labelling kit
(CapitalBio). Double-stranded cDNAs (containing the T7 RNA polymerase promoter
sequence) were synthesised from 1g of total RNA using CbcScript reverse transcriptase
with a cDNA synthesis system according to the manufacturer’s protocol (Capitalbio) with T7
Oligo (dT) and T7 Oligo (dN) primers.
After completion of double-stranded cDNA
(dsDNA) synthesis using DNA polymerase and RNase H, the dsDNA products were purified
using a PCR NucleoSpin Extract II Kit (MN) and eluted with 30L of elution buffer. The
eluted double-stranded cDNA products were vacuum-evaporated to 16L and then subjected
to 40L in vitro transcription reactions at 37°C for 14h using T7 Enzyme Mix.
The
amplified cRNA was purified using an RNA clean-up kit (MN).
Klenow enzyme labelling was adopted after reverse transcription using CbcScript II
reverse transcriptase.
Briefly, 2g of amplified RNA was mixed with 4g of random
nanomer, denatured at 65°C for 5 min, and chilled on ice.
Then, 5L of 4×first-strand
buffer, 2L of 0.1M DTT, and 1.5L of CbcScript II reverse transcriptase were added to the
RNA. The mixtures were incubated first at 25°C for 10min and then at 37°C for 90min.
The cDNA products were purified using the PCR NucleoSpin Extract II Kit (MN) and then
vacuum-evaporated to 14L.
The cDNA was mixed with 4g of random nanomer, heated to
95 C for 3min, and then snap-cooled on ice for 5min.
Five L of Klenow buffer, dNTP and
Cy5-dCTP or Cy3-dCTP (GE Healthcare) were added to yield final concentrations of 240M
dATP, 240M dGTP, 240M dTTP, 120M dCTP, and 40M Cy-dCTP.
1.2 L of Klenow
enzyme was then added to this mixture, and the reaction was performed at 37 C for 90min.
Labelled cDNA was purified with the PCR NucleoSpin Extract II Kit (MN), and
re-suspended in elution buffer.
Labelled controls and test samples labelled with Cy5-dCTP
and Cy3-dCTP were dissolved in 80L of hybridisation solution containing 3×SSC, 0.2%
SDS, 5× Denhardt’s solution and 25% formamide.
The DNA in the hybridisation solution
was denatured at 95°C for 3min prior to loading onto a microarray.
Arrays were hybridised
and pre-formed in a CapitalBio BioMixerTM II Hybridisation Station overnight at a rotation
speed of 8rpm and 42 C and then washed with 0.2% SDS, 2× SSC at 42°C for 5min and 0.2×
SSC at room temperature for 5min.
Data analysis
The lncRNA + mRNA array data were analysed for data summarisation, normalisation and
quality control using GeneSpring V11.5 software (Agilent).
To select differentially
expressed genes, we used threshold values of ≥2- and ≤2-fold change and a
Benjamini-Hochberg corrected p-value of 0.05.
The data was Log2 transformed and median
centred by genes using the Adjust Data function of CLUSTER 3.0 software.
Further
analysis was performed by hierarchical clustering with average linkages.
Finally, we
performed tree visualisation using Java Treeview (Stanford University School of Medicine,
Stanford, CA, USA).
Functional group analysis
We used the http://bioinfo.capitalbio.com/mas3/ system to conduct Gene Ontology (GO) and
pathway analysis.
The GO project provides a controlled vocabulary for describing genes
and gene product attributed to any organism.
The ontology covers three domains: biological
process, cellular component and molecular function.
Pathway analysis is a functional
analysis method that maps genes to Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways.
Statistical analysis was performed to determine the degree of enrichment of the
genes in each pathway.
Construction of the co-expression network
The coding-non-coding gene co-expression network (CNC network) was constructed based
on the correlation analysis between the differentially expressed lncRNA and mRNA.
For
each pair of genes, a Pearson correlation was calculated and the significant correlation pairs
were chosen to construct the network.
LncRNAs and mRNAs with Pearson correlation
coefficients not less than 0.99 were selected to draw the network though the open source
bioinformatics software Cytoscape.
In a network analysis, the degree of centrality is
defined as the number of linkages one node has to the other.
A degree is the simplest and
most important measures of a gene centrality within a network that determining the relative
importance.19
Quantitative real-time PCR
Total RNA was extracted using TRIzol reagent (Invitrogen, CA, USA) and then
reverse-transcribed using the PrimeScript RT Reagent Kit with gDNA Eraser (Perfect Real
Time) (TaKaRa, Dalian, China).
Six lncRNA expressions in tissues were measured by
quantitative PCR (qPCR) using SYBRGreen assays (TaKaRa, Dalian, China).
used as an internal control.
GAPDH was
The primers used in this study were listed in Table 2.
To
obtain quantitative results, the expression of each lncRNA was represented as fold-changes
using the △△Ct method.20
Differences in lncRNA expression were analysed using Student’s
t-test with SPSS (version 16.0, SPSS Inc.). A value of p<0.05 was considered significant.
Results
Overview of the lncRNA profiles
39,253 distinct lncRNA transcripts were detected in the lung tissues of non-smokers (NS),
smokers without COPD (Smokers No COPD, SNC) and smokers with COPD using
microarrays (Supplemental Table 1). SNC patients expressed 5,782 more lncRNAs than NS
and this number was further increased in COPD patients.
Of these, 87 were significantly
(≥2-fold) up-regulated and 244 down-regulated in SNC versus NS (Supplemental Table 2).
Compared with NS patients, RNA50010︱UCSC-9199-1005 (fold-change: 13.02297) and
RNA58351|CombinedLit_316_550 (fold-change: 12.609704) were the most over- and
under-regulated lncRNAs in SNC patients.
In contrast, 120 lncRNAs were overexpressed
and 43 underexpressed in COPD patients compared with SNC (Supplemental Table 3).
RNA44121 ︱ UCSC-2000-3182
(fold-change:
8.721127)
and
RNA43510 ︱
UCSC-1260-3754 (fold-change: 5.527549) were the most over- and under-regulated
lncRNAs in COPD patients compared with SNC subjects.
(RNA42913|UCSC_554_4998,
RNA43477|UCSC_1222_3795
Only 4 lncRNAs
RNA53319|H-InvDB_585_590,
and
RNA49307|UCSC_8363_1189)
were
differently
expressed with statistical significance (Supplemental Table 4) in both SNC versus NS and
between COPD and SNC.
LncRNAs may be associated with the occurrence and development of COPD.
We used
hierarchical clustering analysis to arrange the samples into groups based on their expression
levels, thereby allowing us to hypothesise the relationships among samples.
The
dendrogram in figure 1 shows the relationships between the lncRNA expression patterns
between SNC and NS (Fig. 1A) and between COPD patients and SNC (Fig. 1B).
Overview of the mRNA profiles
32,207 coding transcripts could be detected in the lung tissues of NS, SNC and COPD
patients (Supplemental Table 5).
Among these transcripts, the differential (≥2-fold)
expression of 97 up-regulated and 110 down-regulated mRNAs reached statistical
significance between SNC and NS (Supplemental Table 6).
In addition, 97 mRNA
transcripts were significantly over-expressed and 37 significantly under-expressed in COPD
patients but not in SNC patients (Supplemental Table 7). Clustering analysis shows the
relationships among the mRNA expression patterns between SNC and NS (Fig. 2A) and
between COPD patients and SNC (Fig. 2B).
Overview of the co-expression network
A coding-non-coding gene co-expression network (CNC network) was constructed based on
the correlation analysis between the differentially expressed lncRNAs and mRNAs in COPD
patients compared with healthy smokers.
In this co-expression network, the CNC network
node included 76 lncRNAs and 55 mRNAs (Fig. 3) with lncRNAs and mRNAs interactions
presented as showing positive or negative correlations.
The CNC network indicates that one
mRNA could correlate with one or two lncRNAs and vice versa.
The CNC network
indicates potential internal adjustment mechanisms between different lncRNAs and mRNAs
in COPD.
GO and pathway analyses
GO analysis was performed to determine the genes and gene product enrichment involved in
biological processes, cellular components and molecular functions.
Fisher’s exact test was
used to determine whether or not the overlap between the differentially expressed gene list
and the GO annotation list is greater than that expected.
In the group ‘SNC versus NS’, the highest enriched GOs targeted genes by the
significantly different transcripts were cellular process (ontology: biological process), protein
binding (ontology: molecular function) and cell part (ontology: cellular component) (Figs. 4,
5 and 6). Pathway analysis indicated that 60 pathways corresponded to significantly different
transcripts and that the most enriched network was ‘neuroactive ligand-receptor interaction,’
composed of five targeted genes (Table 3 & Supplementary Table 8).
GO and pathway
analyses showed that some differently expressed mRNAs caused by cigarette smoking are
involved in primary metabolism and key signalling pathways (e.g., calcium signalling
pathway, cytokine-cytokine receptor interaction).
In the group ‘COPD patients versus SNC’, we found that the highest enriched GOs
targeted genes by significantly different transcripts were cellular process (ontology:
biological process), protein binding (ontology: molecular function) and cell part (ontology:
cellular component) (Figs. 4, 5 and 6).
Pathway analysis indicated that 38 pathways
corresponded to significantly different transcripts and that the most enriched network was
‘hematopoietic cell lineage', which was composed of five targeted genes (Table 3 &
Supplementary Table 9). GO and pathway analyses also showed that different mRNAs may
be involved in primary metabolism and immunological signalling pathways.
Real time quantitative PCR validation
We
selected
seven
over-regulated
lncRNAs
(RNA37093
|ENCODE-1963-473,
RNA175499|ENST00000544591
RNA48255|UCSC-7110-1509,
RNA174930|ENST00000508732,
RNA47218|UCSC_5826_1803,
RNA53748|H-InvDB_1025_432,
RNA44480|UCSC_2447_2885,
RNA43329|UCSC_1041_4013,
RNA44021|UCSC_1880_3276
RNA39398|RefSeq_1374_2314,
and
RNA39240|RefSeq_1208_2498)
and
three
under-regulated lncRNAs (RNA165538|XLOC-007769, RNA147089|nc-HOXC11-109 and
RNA35262|ENCODE-120-2690) for confirmation of expression in two sets of lung tissue
samples using qPCR (Table 4). Gene array expression data are expressed as mean values.
The results obtained indicate 71.4% consistency between the qPCR findings and microarray
data.
Discussion
Previous evidence has shown that lncRNAs are important functional molecules engaged in
diverse gene regulatory functions, including many biological and pathological processes.8-14
LncRNAs are closely correlated with a variety of human diseases,14 including cancer and
asthma.21
However, no reports on the expression of lncRNAs in COPD have yet been
published.
In the present study, we investigated lncRNA expression profiles in the lung tissues of
NS, SNC and smokers with COPD using microarray analysis and found that lncRNA
expression levels were statistically significantly altered among these groups.
We found that
hundreds of lncRNAs were differentially expressed (≥2-fold change) in lung tissue from
smokers without COPD compared with non-smokers and in lung tissue from smokers with
COPD and smokers without COPD.
Also, hundreds of differentially expressed lncRNAs
(≥2-fold change) were found in smokers without COPD but not in non-smokers.
results indicate that smoking alters the expression of lncRNAs.
These
GO and pathway analyses
revealed that these lncRNAs are associated with changes in key pathogenic processes of
COPD caused by cigarette smoking.
GO and pathway analyses showed that different lncRNAs and mRNAs are involved in
the metabolism of several cellular mediators and in signalling pathways linked to the altered
immune system in COPD patients.
Recent studies have uncovered the potential roles of
adaptive immunity22-24 and autoimmune responses25 in patients with COPD.
A specific
pattern of inflammation appears to occur in the airways and parenchyma of COPD patients
who smoke, with the predominance of CD8+ and CD4+ cells and, in more severe diseases,
the presence of lymphoid follicles containing B lymphocytes and T cells.26
The strong
agreement between our results and the previous findings suggests that further research on the
defence mechanism of the immune system against COPD will be useful in revealing other
mechanisms related to the disease.
We found that the expression of many lncRNAs is significantly correlated with the
expression of dozens of protein-coding genes through the CNC co-expression network.
These lncRNAs may be involved in the regulatory expression of coding genes in COPD
patients.
Transcription of lncRNAs is now known to regulate the expression of genes in
close genomic proximity (cis-acting regulation) and to target distant transcriptional activators
or repressors (trans-acting) via a variety of mechanisms.7
Several studies have shown that
knockdown or low expression of lncRNAs could lead to the decreased expression of their
neighbouring protein-coding genes.
An over-regulated lncRNA, UCSC_3382_2454, was
found to be positively associated with cytochrome P450 1B1 (CYP1B1) enzymes.
CYP
enzymes play a key role in catalysing specific enzymes associated with breakdown and
removal of deleterious xenobiotics and products of oxidative stress resulting from tobacco
smoking in COPD patients.27
Interestingly, the over-regulated lncRNAs UCSC_2447_2885
and UCSC_2_26019 were found to be negatively correlated with haptoglobin and raised
plasma haptoglobin is associated with the increased incidence of COPD exacerbations
resulting in hospitalisation.28
The function of lncRNAs has been most extensively studied in tumorigenesis where they
play important roles in the regulatory mechanisms of oxidative stress, inflammation,
apoptosis, cell growth and viability.13,15,16 All these processes have also been implicated in
the etiology of COPD.29
In the management of COPD, interleukin-6 (IL-6) is a useful
clinical biomarker predicting worsening exercise tolerance and greater mortality,30 whilst
Perry et al. reported that IL-6 release could be regulated by miR-221, a type of ncRNA, in
airway smooth muscle cells, through modulation of the cyclin-dependent kinase inhibitors,
p21(WAF1) and p27(kip1).31
In our study, induction of RNA175876|ENST00000554946
could affect the progress of inflammation by regulating the neighbouring protein-coding gene
NF-B activating protein 1 (also known as TAB3 or MAP3K7 Binding Protein 3).
Induction of RNA147089|nc-HOXC11-109, which probably appears to regulate the
neighbouring protein-coding gene keratin 7, affects cell growth and viability.
Keratin 7 is a
Type II member of the keratin superfamily, and loss of keratin 7 leads to increased
proliferation of the bladder urothelium.32
under-regulated
lnRNAs,
such
RNA45078|UCSC_3181_2530,
A large number of significantly over- and
as
RNA43329|UCSC_1041_4013,
RNA43297|UCSC_1005_4076
RNA53055|H-InvDB_314_668, are located near the IG gene.
and
These findings are in line
with the results from GO and pathway analyses and suggest the regulation of immune
mechanisms in the progress of COPD.
Although indicating several potentially important pathways in COPD this study has some
limitations.
Firstly the low number of subjects used in each group and the RT-qPCR
analysis of only 10/14 lncRNAs means that validation of these changes needs to be
performed in larger numbers of subjects.
Furthermore, since the studies were performed in
lung tissues and not individual cell types it is possible that the differences seen reflect altered
recruitment and/or activation of infiltrating inflammatory cells in response to disease or
cigarette smoking.
Future experiments will be needed to determine the cellular source of
these lncRNAs using in situ hybridisation and/or laser capture microdissection of single cell
types as there is no validated bioinformatics package to deconvolute the lncRNA data to
assign to individual cell types. Knock down or overexpression of specific lncRNAs leading to
altered expression of mRNAs could then be associated with individual cell types.
Third,
the presence of lung cancer in response to smoking could affect lncRNA expression as
previously described.33 Our experimental data showed the changes of LncRNAs in COPD
patients compared with control group but ideal control group would be samples from
non-smoking lung cancer patients.
In conclusion, our study is the first to determine genome-wide lncRNA expression in
COPD lung in comparison with NS and smokers without COPD by microarray analysis.
Cigarette smoking altered the expression of hundreds of lncNRAs, which might play a role, at
least in part, in the pathological changes generated by cigarette smoking.
We also found
large numbers of lncRNAs differentially expressed in COPD patients independent of smoking
status. GO and pathway analyses also suggested that COPD may have components of
metabolic and systemic immune diseases.
Investigations into the relations between various
signalling pathways and lncRNAs may supply novel insights into the molecular regulation of
COPD and provide new methods for diagnosing and treating COPD.
Further research is
necessary to understand the molecular mechanisms and biological functions of lncRNAs in
COPD.
Acknowledgements:
This study was helped by Dr. Liang Chen and Dr. Quan Zhu for the clinical information
support.
Contributors:
XY conceived and designed the experiments, HB, JZ, FL and DW performed the experiments,
WG and LL analyzed the data, HB and LY wrote the initial draft of the manuscript and MH,
IMA, PJB and XY revised the manuscript and indicated where additional experimental data
was required.
All authors read and approved the final manuscript.
Competing interests:
The authors have declared that no competing interests exist.
Funding: This study was supported by the National Natural Science Foundation of China
(81070025), Jiangsu Health Promotion Project, and the Priority Academic Program
Development of Jiangsu Higher Education Institutions (PAPD, JX10231801).
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Table 1. Clinical characteristics of volunteers
Parameters
Healthy non-smokers
Healthy smokers
COPD
3/0
5/0
5/0
58±8.5
65.6±7.9
72.4±9.2
0
23.7±1.4
29.8±2.3
Non-smoker
Current smoker
Current smoker
FEV1% predicated
NA
108.7±7.3
68.4±1.2
FEV1/FVC
NA
98.3±1.4
67.2±0.8
24.2±1.6
24.4±2.2
25.2±3.4
Sex(M/F)
Age (years)
Smoking status
Pack year
Current smoker/
former/
non-smoke
BMI
All data are presented as meanSD
NA = not available
FEV1 = forced expiratory volume in 1 second
FVC = forced vital capacity
BMI = body mass index
Table 2. PCR Primers used
Primer
RNA147089|nc-HOXC11
sequence(5'to 3')
Forward- CAGGAACTCATGAGCGTGAA
Reverse- TCATGTAAAGCAGCCACAGC
RNA165538|XLOC_007769
Forward- ACAGGAGGTGGTGTCTCTGG
Reverse- CTGGTGCTGAGTGAGCCATA
RNA175499|ENST00000544591
Forward- ATGCCAAGAATCTGGACACC
Reverse- TTCTGTCCCATCGTGTTCAA
RNA37093|ENCODE_1963_473
Forward- GATGGGGTTCCACCATGTAG
Reverse- GGGTGGCACATGTTATGGTT
RNA48255|UCSC_7110_1509
Forward- CGAGAGTAGGAGTGAGGCAG
Reverse- ATCTGGTTTCCTGGTAGCCC
RNA35262|ENCODE_120_2690
RNA174930|ENST00000508732
RNA47218|UCSC_5826_1803
RNA53748|H-InvDB_1025_432
RNA44480|UCSC_2447_2885
RNA43329|UCSC_1041_4013
RNA39398|RefSeq_1374_2314
RNA44021|UCSC_1880_3276
RNA39240|RefSeq_1208_2498
Forward- CTGGGATTACAGGCGTGAGT
Reverse- GCTTCTACGCCACCTCAGTC
Forward-ATCCTCGGTGCTGTGACTCT
Reverse-ATTTCCCCACAAACACCAAA
Forward-GGTGTTGGGAAAACTGGCTA
Reverse-GCCATTGCTTTTGGTGTTTT
Forward-CCGTCACTCCATATGTCACG
Reverse-GCTGCTTTTCCTTTCAGGTG
Forward-CCCAGTTCATGGCTACTGGT
Reverse-GTTCCAAGGAGGGTCTGTCA
Forward-AAGAGCCTCTCCCTGTCTCC
Reverse-GGCACTGTAGCACACGCTTA
Forward-ATCTCCGTGCTGAAGCTGTT
Reverse-AGCTGTATCCCACCAACCAG
Forward-CCAGCGACACAGAAGTTTCA
Reverse-AGGCTGCAGGCTGAGTGTAT
Forward-ACGTGTTCCCTCATCTGTCC
Reverse-TTACAGACGTGAGCCACTGC
Table 3.
Pathway analysis between Smokers without COPD (SNC), non-smokers (NS) and COPD
patients.
SNC versus NS
Pathway
COPD versus SNC
Count
p-Value
q-Value
5
1.74E-04
4.96E-05
Neuroactive ligand-receptor
Hematopoietic
interaction
Count
p-Value
q-Value
5
1.02E-07
8.53E-06
3
8.38E-05
0.001005105
3
1.28E-04
0.00134719
3
7.06E-04
0.003953134
2
9.44E-04
0.004957273
2
0.003723262
0.011583481
2
0.0165396
0.029560137
2
0.023637951
0.036770146
2
0.043219276
0.050422489
cell
lineage
Calcium signaling pathway
Leukocyte
Pathway
4
5.34E-04
1.07E-04
3
0.0019025
1.46E-04
transendothelial
Retinol metabolism
B cell receptor signaling
migration
pathway
Cell adhesion molecules
Folate biosynthesis
2
0.0020231
1.50E-04
(CAMs)
Cell
adhesion
molecules
Primary
3
0.002542
1.75E-04
(CAMs)
immunodeficiency
Metabolism
of
xenobiotics
by
Natural killer cell mediated
3
0.0027624
1.78E-04
cytotoxicity
cytochrome P450
ABC transporters - General
2
0.0035731
2.04E-04
2
0.0055292
2.91E-04
Pathogenic Escherichia coli
Purine metabolism
Calcium
infection - EHEC
signaling
pathway
Neuroactive
Pathogenic Escherichia coli
2
0.0055292
2.91E-04
ligand-receptor
infection - EPEC
interaction
Inositol
phosphate
2
0.0055292
2.91E-04
2
0.0063377
3.07E-04
2
0.0081051
3.52E-04
Renal cell carcinoma
2
0.0090623
3.86E-04
Chronic myeloid leukemia
2
0.0100675
3.92E-04
2
0.0103262
3.92E-04
ECM-receptor interaction
2
0.0125004
4.03E-04
Small cell lung cancer
2
0.0130725
4.12E-04
Hematopoietic cell lineage
2
0.0133628
4.12E-04
3
0.0159888
4.38E-04
GnRH signaling pathway
2
0.0197399
4.76E-04
Wnt signaling pathway
2
0.0377008
6.01E-04
metabolism
Acute myeloid leukemia
Adipocytokine
signaling
pathway
Phosphatidylinositol
signaling system
Cytokine-cytokine
receptor
interaction
Table 4. Validation of gene array expression data by RT-qPCR
ProbeName
RNA37093|ENCODE_1963_473
RNA175499|ENST00000544591
RNA48255|UCSC_7110_1509
RNA174930|ENST00000508732
RNA47218|UCSC_5826_1803
RNA53748|H-InvDB_1025_432
RNA44480|UCSC_2447_2885
RNA43329|UCSC_1041_4013
RNA39398|RefSeq_1374_2314
RNA44021|UCSC_1880_3276
RNA39240|RefSeq_1208_2498
RNA165538|XLOC_007769
RNA147089|nc-HOXC11-109
RNA35262|ENCODE_120_2690
P Value
gene array
0.0038056
0.0268387
0.004614
5.0455465
3.632206
2.7927952
P Value
(RT-qPCR)
0.0293
0.8639
0.108
0.018148698
2.77533
0.0203
up
0.002129219
0.004474363
0.014477932
0.00621317
0.02302896
0.01373219
0.0010184
0.0067393
0.0081903
0.0371716
1.98455
2.24561
2.40442
2.4931402
5.3074193
2.1264117
2.0241427
3.0975757
3.0646358
2.9544559
0.0103
0.0037
0.027
0.0209
0.124
0.0076
0.0261
0.0211
0.0055
0.642
up
up
up
up
up
up
up
down
down
down
Fold change
Regulation
up
up
up
Figure legends:
Figure 1. Heat maps showing the differential expression and hierarchical clustering of
lncRNAs in (A) smokers with no COPD (SNC) compared with non-smokers (NS) and in (B)
COPD patients compared with SNC.
Figure 2. Heat maps showing the differential expression and hierarchical clustering of
mRNAs in (A) smokers with no COPD (SNC) compared with non-smokers (NS) and in (B)
COPD patients compared with SNC.
Figure 3. Coding-non-coding gene co-expression network (CNC network) between the
differentially expressed lncRNAs and mRNAs in COPD patients compared with smokers
without COPD.
The green spots represent mRNAs and the yellow spots represent lncRNAs.
Red lines represent a positive interaction between transcripts and blue lines a negative
interaction.
Figure 4. The biological process of enriched GOs targeted genes by the significantly
different transcripts in (A) smokers with no COPD (SNC) compared with non-smokers (NS)
and (B) COPD compared with SNC.
Figure 5. The molecular function of enriched GOs targeted genes by the significantly
different transcripts in (A) smokers with no COPD (SNC) compared with non-smokers (NS)
and (B) COPD compared with SNC.
Figure 6. The cellular component of enriched GOs targeted genes by the significantly
different transcripts in (A) smokers with no COPD (SNC) compared with non-smokers (NS)
and (B) COPD compared with SNC.