Abstract
Epigenetic modifications are common in chronic obstructive pulmonary
disease (COPD); however, their clinical relevance is largely unknown.
We hypothesized that epigenetic disruptions are associated with
symptoms and health status in COPD. We profiled the blood (n = 57) and
airways (n = 62) of COPD patients for DNA methylation (n = 55 paired).
The patients’ health status was assessed using the St. George’s
Respiratory Questionnaire (SGRQ). We conducted differential methylation
analyses and identified pathways characterized by epigenetic
disruptions associated with SGRQ scores and its individual domains.
29,211 and 5044 differentially methylated positions (DMPs) were
associated with total SGRQ scores in blood and airway samples,
respectively. The activity, impact, and symptom domains were associated
with 9161, 25,689 and 17,293 DMPs in blood, respectively; and 4674,
3730 and 5063 DMPs in airways, respectively. There was a substantial
overlap of DMPs between airway and blood. DMPs were enriched for
pathways related to common co-morbidities of COPD (e.g., ageing, cancer
and neurological) in both tissues. Health status in COPD is associated
with airway and systemic epigenetic changes especially in pathways
related to co-morbidities of COPD. There are more blood DMPs than in
the airways suggesting that blood epigenome is a promising source to
discover biomarkers for clinical outcomes in COPD.
Keywords: epigenetics, COPD, blood, airway, SGRQ
1. Introduction
Chronic obstructive pulmonary disease (COPD) is characterized by
persistent airflow obstruction and shortness of breath [[48]1]. This
condition affects 384 million persons and is responsible for over 3
million deaths worldwide [[49]2]. Although the pathogenesis of COPD has
not been fully elucidated, it is caused by a complex interaction
between environmental and genetic factors [[50]3]. For example,
cigarette smoking, which is the leading known risk factor for COPD, can
alter gene expression, which may be mediated through epigenetic
mechanisms. We have previously shown that the airway epithelium of COPD
patients harbors a unique DNA methylation profile [[51]4] and can alter
gene expression without changing the DNA sequence. Whether these
changes are local (i.e., in the small airways) or systemic (i.e., also
reflected in blood) are uncertain. Moreover, their influence on
patient-related outcomes such as symptoms or health status is also not
known.
The St. George’s Respiratory Questionnaire (SGRQ) is a commonly used
instrument, which captures the impact of disease (and its symptoms) on
the quality of life of patients with COPD [[52]5,[53]6]. SGRQ is a
50-item questionnaire built on three domains: symptoms (frequency and
severity of respiratory symptoms), activity (the effect of
breathlessness on mobility and physical activity), and impact (the
influence of disease on the psychosocial aspects of life). This tool is
also used to assess the potential benefits of a treatment. A reduction
of 4 units in the total SGRQ score is considered the minimum clinically
important difference [[54]7]. The molecular mechanisms underlying
quality of life in COPD are not well understood. Here, we hypothesized
that epigenetic dysregulation contributes to worsening health status in
COPD patients and because COPD is a systemic disease, we also posited
that blood will contain more epigenomic changes than in the airways. To
investigate our hypothesis we conducted epigenome-wide differential
methylation analyses to determine the association of blood and airway
DNA methylation profiles with total SGRQ scores and its domains in COPD
patients; we then compared the blood and airway epigenetic signatures
and identified important pathways characterized by differential
methylation.
2. Materials and Methods
2.1. Differential Effects of Inhaled Symbicort and Advair on Lung Microbiota
(DISARM) Study Cohort
For this investigation we used the DISARM study, a 12-week randomized
control trial (ClinicalTrials.gov [[55]NCT02833480]) conducted in two
hospitals in Vancouver, British Columbia, Canada (St. Paul’s Hospital
and the British Columbia Cancer Agency). Institutional ethics approval
was obtained from the University of British Columbia/the Providence
Health Care Research Ethics Committee (H14-02277). This study has been
fully described previously [[56]8,[57]9,[58]10,[59]11]. In brief,
DISARM enrolled 89 stable COPD patients, and 63 of these patients
reached the bronchoscopy stage of the study. The initial bronchoscopy
was performed with the patient free of any inhaled corticosteroid
(ICS)-based therapy for at least 4 weeks and were clinically stable for
at least 8 weeks prior to the procedure. Bronchial brush samples were
obtained from the 6th–8th generation airways (typically in the right or
left upper lobes). Blood samples were also collected at the initial
bronchoscopy visit. Spirometry was performed according to the
recommendations of ATS/ERS [[60]12]. Health-related quality of life was
ascertained using the SGRQ 1-week following the bronchoscopy. Patients
provided baseline demographic information including medications and
comorbidities and also underwent pulmonary function tests. For the
present study, we retained a total of 64 patients; of these 57 provided
blood and 62 underwent bronchoscopy for DNA methylation profiling; and
55 of these patients had paired blood and brushing samples. A study
diagram is shown in [61]Supplemental Figure S1.
2.2. DNA Methylation Profiling
For all participants, DNA extracts were obtained from peripheral blood
(buffy coat fraction) and airway epithelial cell samples using the
DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany). Unmethylated
cytosine residues present in the DNA extracts were converted to uracil
using the EZ DNA Methylation Kit (Zymo, Irvine, CA, USA). The Illumina
Infinium MethylationEPIC BeadChip microarray was then used to profile
863,904 DNA methylation sites (CpG probes). All samples were profiled
in one run and were randomized within the chip; this step was performed
by technicians blinded to the patients’ clinical characteristics. To
ensure that the blood and airway profiles were comparable we processed
the data together according to previously described methods
[[62]4,[63]13,[64]14,[65]15,[66]16]. The beta values for the CpG probes
were calculated as the ratio of methylation probe intensity to the
overall intensity ranging from 0 (fully unmethylated) to 1 (fully
methylated). CpG probes with a detection quality of p > 1 × 10^−10, or
contained non-CpGs, single nucleotide polymorphisms, or
cross-hybridization probes were removed from the downstream analyses.
Background correction, normalization, and batch correction steps were
applied using the Normal-exponential out-of-band [[67]17], Beta-Mixture
Quantile Normalization [[68]18], and ComBat [[69]19] methods,
respectively.
2.3. Differential Methylation Analysis
We conducted epigenome-wide association analyses based on the blood and
airway epithelial cell DNA methylation profiles. For each tissue type
we used the EPISTRUCTURE algorithm [[70]20] to infer the population
structure in our data. This software calculates principal components
(PCs) based on CpGs that are highly correlated with a single nucleotide
polymorphism to capture the genetic variability within a population.
Blood cell proportions were estimated based on the deconvolution method
by Houseman et al. [[71]21] as implemented in the Horvath laboratory
webtool ([72]https://dnamage.genetics.ucla.edu/home, accessed on 10
August 2022). In addition, we conducted a PC analysis based on the
entire blood DNA methylation profiles and correlated the first two PCs
with blood cell proportions to assess their effect on blood DNA
methylation; we included only the cell components with significant
correlations with at least one PC (p < 0.05) in the downstream
analyses, which included CD8pCD28nCD45Ran [memory and effector T
cells], plasmablast [plasmablasts], CD4T [CD4 lymphocytes], NK [natural
killer cells], Mono [monocytes] and Gran [granulocytes]). To identify
differentially methylated positions (DMPs) associated with SGRQ scores,
we used a robust linear model (rlm) function implemented in the MASS R
package [[73]22]. For these analyses, the beta values were logit
transformed to M-values (normal distribution). Additional covariates
(i.e., baseline age, sex, body mass index and smoking status) were
included according to the algorithm as outlined by Lee et al.
[[74]4,[75]14,[76]23]. Considering the number of variables in our
model, the limited sample size of our cohort and the fact that baseline
FEV[1]% predicted was significantly correlated with SGRQ total score (R
= −0.47, p = 0.001), we did not adjust our analyses for lung function.
Our model for the blood cell DNA methylation profiles was defined as:
[MATH:
Methylation
(M values)~ SGRQ
total score+Age+Sex+BMI+<
mi>Smoking status+CD8pCD28nCD45RAn + PlasmaBlast + CD4T + NK + Mono + Gran+EPISTRUCTURE <
mrow>(PC1 to<
/mi> PC5) :MATH]
The differential analysis for airway epithelial cells profiles was
defined as:
[MATH:
Methylation
(M values) ~ S
GRQ total score+Age+Sex+BMI<
mo>+Smoking status+ EPISTR
UCTURE (PC
1 to PC5) :MATH]
These analyses were conducted to assess the association between DNA
methylation in the two tissues and SGRQ scores and its domains
([77]Supplementary Figure S1). Significant DMPs were defined based on a
false discovery rate (FDR) cut-off of <0.10. We later used the R
package DMRcate [[78]24] to identify differentially methylated regions
(DMR), which were defined with at least three consecutive CpGs.
2.4. Pathway Enrichment Analysis
We used the software package WebGestaltR to identify Kyoto Encyclopedia
of Genes and Genomes (KEGG) pathways enriched by the genes
characterized by differential methylation in blood and airway
epithelial cells. Significant enrichment was defined at FDR < 0.05.
3. Results
3.1. Study Cohort Overview
An overview of the study cohort is presented in [79]Table 1. The
participants included a total of 64 adults, of these 57 were profiled
for blood and 62 had airway samples; the majority of whom were males.
Table 1.
Demographic characteristics at baseline.
DISARM Study Cohort
n 64
Age, years 64 ± 8
Female, % 17
BMI, kg/m^2 24.58 (21.09–29.35)
Smoking status
Current, % 45
Former, % 55
Pack per years 48.00 (33.00–59.50)
SGRQ total score 44.06 (33.08–54.90)
SGRQ activity score 65.89 (48.52–74.08)
SGRQ impacts score 28.45 (18.10–39.68)
SGRQ symptoms score 56.20 (39.30–70.63)
FEV[1]% of predicted 55.00 (43.65–67.25)
FVC% of predicted 83.35 (71.90–92.25)
FEV[1]/FVC, percent 52.64 (42.93–60.09)
[80]Open in a new tab
BMI: body mass index. FEV1: forced expiratory volume in 1 s. FVC:
Forced vital capacity. Age is shown as mean and standard deviation.
Count data are shown in percentages, and Lung function variables are
reported as median and interquartile range as variables are not
normally distributed.
3.2. Blood and Airway Epigenetic Disruptions Are Associated with SGRQ Scores
Differential blood DNA methylation analysis based on DNA methylation
profiles yielded 29,211 DMPs associated with total SGRQ scores
([81]Figure 1A, [82]Supplementary Table S1). These DMPs were within the
vicinity of 13,485 unique genes. [83]Table 2 shows that the top five
DMPs identified in blood were located within the ARFGAP1,
RP11-711C17.2, PPARG and MGAT4C genes ([84]Figure 1A). In blood, we
identified 3250 DMRs associated with total SGRQ scores
([85]Supplementary Table S2); [86]Table 3 shows the top five DMRs.
Differentially methylated genes for total SGRQ score were enriched in
119 pathways ([87]Figure 2A, [88]Supplementary Table S3), including
cancer pathways (e.g., small and non-small cell lung cancer),
age-related pathways (e.g: longevity regulating pathway and mTOR
signaling pathway), and neurological pathways (e.g., cholinergic
synapse and dopaminergic synapse). The SGRQ activity, impact, and
symptom domains were associated with 9161, 25,689 and 17,293 DMPs in
blood, respectively. Activity score DMPs corresponded to 5508 unique
genes that enriched 19 pathways (e.g., pathways in cancer, longevity
regulating pathway and oxytocin signaling pathway); impact score DMPs
corresponded to 11,901 genes that enriched 115 pathways (e.g., pathways
in cancer, MAPK signaling pathway and PI3K-Akt signaling pathway); and
symptom score DMPs were located within 8332 genes that enriched 75
pathways (e.g., platelet activation, Inflammatory mediator regulation
of TRP channels and cortisol synthesis and secretion). Furthermore,
2087 genes were exclusive to activity DMPs, 6181 genes to impact DMPs
and 3581 genes to symptom DMPs ([89]Supplemental Figure S2). The top
genes characterized by differential methylation in blood for each SGRQ
domain are shown in [90]Table 2, and included CCDC30, DOCK2 and F2 for
the activity score DMPs, ERC2, AC004041.2, RAD50, and AP4S1 for the
impact score DMPs, and BACH2 and WNK2 for the symptom score. We also
found 1048, 2925, and 1924 DMRs for activity, impact, and symptom
score, respectively; [91]Table 3 shows the top five DMRs.
Figure 1.
[92]Figure 1
[93]Open in a new tab
Blood (A) and airway (B) differentially methylated sites association
with Total SGRQ score. x-axis represents the robust linear model (rlm)
estimated effects on the methylation Beta-values, y-axis represents the
rlm −log10P on M-values. For each unit of SGRQ increased, DNA
methylation decreases (hypomethylation = blue) or increases
(hypermethylation = red).
Table 2.
Top five blood and airway DMPs for SGRQ scores.
SGRQ Score Tissue Probe Beta Difference p FDR Chr Relation to Island
Position in Relation to Gene Gene Symbol
Total Blood cg00542760 −0.0002 5.22 × 10^−25 2.06 × 10^−19 20 Island
5’UTR; 1stExon; TSS1500; 3’UTR ARFGAP1
cg02344187 −0.0003 3.21 × 10^−25 2.06 × 10^−19 12 Open Sea 5’UTR
RP11-711C17.2
cg25911248 −0.0004 2.78 × 10^−23 7.32 × 10^−18 3 Open Sea 3’UTR PPARG
cg00151915 −0.0003 6.59 × 10^−23 1.30 × 10^−17 12 Open Sea 5’UTR MGAT4C
cg02213440 −0.0003 1.49 × 10^−20 2.35 × 10^−15 7 Open Sea
Airway cg16929656 0.0026 1.44 × 10^−17 1.13 × 10^−11 19 Open Sea 3’UTR
PPP5C
cg05245430 0.0002 6.08 × 10^−15 2.40 × 10^−09 14 Island 3’UTR; 1stExon
CCDC88C
cg21153875 −0.0001 9.40 × 10^−15 2.47 × 10^−09 1 Island TSS200
C1orf187; MAD2L2
cg15346134 0.0014 6.01 × 10^−14 1.18 × 10^−08 1 Open Sea TSS200 EPHA2
cg15550234 0.0021 1.78 × 10^−12 2.81 × 10^−07 10 Open Sea 3’UTR; 5’UTR
CPXM2
Activity Blood cg09711814 −0.0001 4.35 × 10^−22 3.43 × 10^−16 7 Open
Sea
cg24639069 −0.0002 1.76 × 10^−18 6.94 × 10^−13 1 Open Sea TSS1500;
5’UTR CCDC30
cg10677105 −0.0005 6.97 × 10^−15 1.83 × 10^−09 5 Open Sea 5’UTR; 3’UTR
DOCK2
cg11893552 0.0015 1.86 × 10^−14 3.66 × 10^−09 6 Open Sea
cg00371195 −0.0001 3.40 × 10^−14 4.47 × 10^−09 11 Open Sea TSS1500 F2
Airway cg00278597 0.0002 4.35 × 10^−12 1.71 × 10^−06 8 Open Sea TSS1500
RP11-1057B8.2
cg09397653 0.0009 2.60 × 10^−12 1.71 × 10^−06 15 Open Sea TSS1500
ITGA11
cg27547307 0.0005 1.11 × 10^−11 2.91 × 10^−06 17 Open Sea TSS1500;
5’UTR CYTH1
cg00413620 0.0011 1.74 × 10^−11 3.43 × 10^−06 1 Open Sea
cg04926227 0.0011 4.02 × 10^−11 6.33 × 10^−06 8 Open Sea TSS1500;
3’UTR; 5’UTR RP11-463D19.1; STAU2
Impact Blood cg23444468 0.0004 6.72 × 10^−30 5.29 × 10^−24 3 Island
TSS1500; 5’UTR ERC2
cg13886298 −0.0002 1.45 × 10^−24 5.70 × 10^−19 5 Open Sea TSS1500;
3’UTR AC004041.2; RAD50
cg15751204 0.0008 5.45 × 10^−22 1.43 × 10^−16 3 Open Sea
cg00851837 −0.0003 4.31 × 10^−20 8.48 × 10^−15 14 Open Sea TSS200 AP4S1
cg15534855 0.0005 1.24 × 10^−19 1.95 × 10^−14 18 Island
Airway cg08738303 0.0005 3.69 × 10^−13 2.91 × 10^−07 18 Open Sea
cg01585096 0.0012 3.64 × 10^−12 7.16 × 10^−07 19 Open Sea TSS200; 3’UTR
KRTDAP
cg03053018 0.0030 2.11 × 10^−12 7.16 × 10^−07 7 Open Sea
cg20447038 0.0018 3.22 × 10^−12 7.16 × 10^−07 6 Open Sea
cg02065293 0.0011 1.08 × 10^−11 1.46 × 10^−06 2 Open Sea
Symptom Blood cg06894541 0.0003 3.10 × 10^−19 2.44 × 10^−13 2 Open Sea
cg25670076 0.0021 4.09 × 10^−18 1.61 × 10^−12 6 Open Sea 5’UTR; 3’UTR
BACH2
cg11743078 −0.0004 1.87 × 10^−15 4.92 × 10^−10 1 Open Sea
cg02415617 −0.0004 2.08 × 10^−14 3.25 × 10^−09 9 South Shore 1stExon;
3’UTR; 5’UTR WNK2
cg04028140 0.0007 2.48 × 10^−14 3.25 × 10^−09 11 Open Sea
Airway cg07380540 −0.0010 6.04 × 10^−26 4.76 × 10^−20 1 South Shelf
cg10789584 0.0005 2.76 × 10^−17 1.09 × 10^−11 11 Open Sea 5’UTR CD82
cg18910215 −0.0007 5.49 × 10^−14 1.44 × 10^−08 9 Open Sea 5’UTR MAPKAP1
cg20708037 0.0018 9.37 × 10^−14 1.85 × 10^−08 1 Open Sea
cg21088488 0.0008 4.81 × 10^−12 7.58 × 10^−07 7 South Shore 3’UTR;
TSS1500 DBNL; PGAM2
[94]Open in a new tab
Top CpGs criteria: smallest to largest FDR. Beta difference was
estimated from each methylation site beta value and p-value was
estimated from each methylation site M-value. Negative beta: for each
unit of SGRQ increased, DNA methylation decreases. Positive beta: for
each unit of SGRQ increased DNA methylation increases.
Table 3.
Top five blood and airway DMRs for SGRQ scores.
SGRQ Score Tissue Chr Start End # CpGs Min
FDR Gene Symbols
Total Blood 3 47,823,638 47,825,578 7 4.71 × 10^−27 SMARCC1
12 124,246,976 124,248,926 5 8.50 × 10^−24 DNAH10
20 61,917,085 61,918,367 5 9.22 × 10^−23 ARFGAP1, MIR4326
4 148,653,624 148,654,701 5 7.03 × 10^−20 ARHGAP10
5 126,779,737 126,780,974 4 1.47 × 10^−19 MEGF10
Airway 18 56,296,094 56,296,607 10 2.90 × 10^−22 ALPK2, RPL9P31
9 91,604,473 91,605,695 7 5.88 × 10^−18 C9orf47, S1PR3
19 46,894,811 46,895,714 3 1.41 × 10^−16 AC007193.8
12 11,698,534 11,699,363 5 3.03 × 10^−14 RP11-434C1.1, RNU7-60P
1 120,173,989 120,175,029 7 3.03 × 10^−14
Activity Blood 11 2,019,436 2,021,103 32 9.13 × 10^−19 H19
6 168,045,268 168,046,457 6 4.18 × 10^−18
3 30,936,070 30,936,955 11 4.98 × 10^−14 GADL1
17 699,291 700,672 4 5.21 × 10^−13
7 94,285,270 94,287,242 60 1.11 × 10^−12 SGCE, PEG10
Airway 18 56,296,094 56,296,607 10 5.98 × 10^−23 ALPK2, RPL9P31
11 86,085,026 86,086,489 12 1.24 × 10^−13 CCDC81
19 29,217,858 29,218,774 7 1.31 × 10^−11 AC005307.3
18 3,411,487 3,412,713 11 1.37 × 10^−11 TGIF1
7 157,866,683 157,868,361 13 2.33 × 10^−11
Impact Blood 6 42,927,199 42,928,920 31 6.89 × 10^−33 GNMT
3 56,501,352 56,502,814 12 1.26 × 10^−30 ERC2
18 74,960,629 74,963,364 35 6.35 × 10^−27 GALR1
10 134,598,316 134,601,851 37 4.30 × 10^−24 NKX6-2, RP11-288G11.3
3 47,823,674 47,825,578 6 1.03 × 10^−23 SMARCC1
Airway 19 35,981,224 35,982,442 10 1.20 × 10^−19 KRTDAP
17 75,470,567 75,472,168 12 2.22 × 10^−15 SEPT9, RP11-75C10.9
21 43,315,518 43,316,705 6 9.74 × 10^−14
12 11,698,367 11,699,363 6 2.63 × 10^−13 RP11-434C1.1, RNU7-60P
3 113,160,071 113,161,177 14 1.70 × 10^−12 WDR52
Symptom Blood 6 32,807,895 32,811,521 30 3.27 × 10^−24 PSMB8, TAP2,
PSMB9, TAPSAR1
5 78,364,769 78,366,302 14 2.24 × 10^−22 DMGDH, BHMT2
6 30,850,207 30,852,354 24 2.24 × 10^−22 DDR1
2 110,969,641 110,970,909 8 4.86 × 10^−22 LINC00116
2 98,329,337 98,330,493 10 1.96 × 10^−21 ZAP70
Airway 6 33,244,976 33,246,895 44 1.04 × 10^−22 B3GALT4, WDR46, RPS18
12 63,025,490 63,026,424 7 2.02 × 10^−21
9 139,425,582 139,427,171 5 5.63 × 10^−20
17 46,655,164 46,656,572 20 6.39 × 10^−17 HOXB4, MIR10A, HOXB3
2 161,992,157 161,993,364 6 1.19 × 10^−16 TANK
[95]Open in a new tab
Top differentially methylated region criteria: smallest to largest
minimum FDR.
Figure 2.
[96]Figure 2
[97]Open in a new tab
KEGG pathways enriched for differentially methylated genes for total
SGRQ score. Horizontal and vertical axis represent the percentage of
genes within each pathway that are characterized by differential
methylation and description of the pathways, respectively. (A) Blood.
(B) Airway.
Airway differential DNA methylation was associated with SGRQ scores,
albeit less than in the blood. For instance, 5044 DMPs were associated
with total SGRQ score ([98]Figure 2B), which corresponded to 2950
unique genes that enriched 38 pathways (e.g., small and non-small lung
cancer, mTOR signalling pathway and insulin resistance). In addition,
we identified 643 DMRs for total SGRQ score in the airway; top five
DMRs are shown in [99]Table 3. For the SGRQ activity score, we
identified 4674 DMPs located within 2847 genes, which enriched 36
pathways (e.g., T and B cell receptor signaling pathways, and longevity
regulating pathway); DMPs were grouped into 590 DMRs. For the impact
score, we identified 3730 DMPs within 2198 genes, which enriched 8
pathways (e.g., Insulin signaling pathway, and mTOR signaling pathway);
in addition we also identified 473 DMRs for the impact score. The
symptom score was associated 5063 DMPs, these were located within 2850
genes that significantly enriched 24 pathways (e.g., platelet
activation and cortisol synthesis and secretion); furthermore 625 DMRs
were identified for the symptom score. In addition, we found that 2039
genes were unique to activity score DMPs, 1257 genes to impact score
DMPs, and 1919 genes to symptom score DMPs ([100]Supplemental Figure
S3).
3.3. A Systemic Epigenetic Signature of Health Status in COPD
We compared differentially methylated genes (DMGs) in blood to those in
the airway epithelium. For DMGs associated with total SGRQ score, there
were 1590 overlapping genes, which represented 54% of the airway
epithelial DMGs ([101]Figure 3A). These genes enriched 25 pathways
([102]Figure 3E), most of these were captured by the blood differential
methylation analyses (24 pathways). These pathways included many aging
(e.g., PI3K-Akt signaling pathway, longevity regulating pathway, and
Ras signaling pathway) and cancer (e.g., non-small and small cell lung
cancer) pathways. For DMGs associated with SGRQ domain scores, there
were 779, 1154 and 1156 overlapping genes for activity, impacts and
symptoms scores, respectively, representing 27%, 53%, and 41% of the
airway epithelial DMGs, respectively ([103]Figure 3B–D).
Figure 3.
[104]Figure 3
[105]Open in a new tab
Differentially methylated genes overlapped between tissues. Venn
diagrams show the overlap of differentially methylated genes identified
in blood versus those in the airway for the total SGRQ score (A), and
its domains: activity (B), impact (C) and symptom (D) scores. (E) shows
the pathways enriched by the differentially methylated genes identified
in blood and airway tissue.
We also compared pathways enriched for DMGs between blood and airway
epithelium. For total SGRQ score, 36 out of the 38 pathways identified
in the airway epithelium ([106]Figure 2B) overlapped with those in
blood, including small and non-small cell lung cancer and mTOR
signaling pathways. For SGRQ activity score, 9 out of the 36 pathways
identified in the airway overlapped with those in blood (e.g., pathways
in cancer and longevity regulating pathway); for SGRQ impact score, 7
out of the 8 pathways identified in the airway epithelium overlapped
with those identified in blood (e.g., mTOR signaling pathway and
insulin signaling pathway); for SGRQ symptom score, we identified 20
out of 24 pathways in the airway epithelium that overlapped with blood
pathways, including platelet activation, pathways in cancer and Wnt
signaling pathway.
4. Discussion
To our knowledge this the first report that directly evaluated blood
and airway epigenetic signatures in relation to health status of
patients with COPD. We made several novel observations. First, there
are distinct epigenetic signatures that relate to health status and
symptoms of patients with COPD. These signatures (both in blood and in
the airway) are enriched in pathways related to accelerated ageing and
lung cancer, which are important consequences and comorbidities of COPD
[[107]25]. Second, although blood carries most of the epigenetic
changes observed in the airways, it also harbors distinct non-airway
related epigenetic changes, which may reflect the systemic nature of
COPD [[108]26].
Epigenome-wide disruptions have been associated with COPD [[109]4];
however their clinical impact has not been well characterized. Our
findings suggest that differential methylation in blood and airway
epithelial cells is associated with patient symptoms and health status
in COPD and that these changes can be detected in blood as well as
airway samples. Our analyses also highlight several interesting
differentially methylated genes, which may have plausible effects in
COPD. One of the most significant genes in the blood differential
analyses was PPARG, where increased methylation within PPARG in the
airways was associated with increased score in the SGRQ symptoms
domain. This gene has anti-inflammatory functions in various cells in
the lung including: airway epithelial cells, endothelial cells, airway
smooth muscle cells, alveolar macrophages and eosinophils
[[110]27,[111]28,[112]29,[113]30,[114]31,[115]32,[116]33,[117]34,[118]3
5]. Mice experiments have shown that activation of PPARG downregulates
the expression of inflammatory chemokines and mitigates
cigarette-smoking induced emphysema [[119]36]. COPD is related to both
airway and systemic inflammation [[120]37,[121]38]. We found that some
of the genes in the inflammatory pathways (e.g., STAT3, PIAS3, IL8
[blood—all DMPs are hypermethylated], IL6 [blood—all DMPs are
hypomethylated], and IL6R [airway and blood—specific DMPs are hypo- or
hypermethylated], IL10 [blood—all DMPs are hypomethylated]) were
differentially methylated and related to health status of our patients.
In support of these findings, in vitro model has linked hypomethylation
of promoters in the NF-κB and STAT3 genes with the induction of
inflammation by lipopolysaccharide and cigarette smoke extract
[[122]39].
Multiple pathways were enriched by the differentially methylated genes
in both blood and airways. Overlapping pathways included age-related
processes such as mTOR signaling pathway, which regulates cell
proliferation, apoptosis, and autophagy in the cellular senescence
process [[123]40] and the PI3K-Akt signaling pathway. Akt activation,
which occurs during ageing, may be responsible for neuronal dysfunction
of ageing [[124]41]. Akt is also involved in the regulation of mTOR
[[125]42]. In addition, cancer pathways were identified in all our
enrichment analyses (e.g., lung cancer). It is well established that
COPD is a major risk factor for lung cancer, increasing its risk by 2
to 4 fold [[126]43]. Our findings suggest that methylation changes in
the genome may be responsible for some of this excess risk, however
more research is needed to define the effect of hypo- and hyper-
methylated genes on cancer risk. Our analyses also highlighted
neurological pathways, for example, dopaminergic synapse, which plays a
central role in the control of behavioral processes such as addiction
and stress [[127]44]. Furthermore, dopamine has been associated with
improvement of diaphragmatic function in COPD patients [[128]45], thus
its regulation may also affect respiratory symptoms. We also found that
there was epigenomic changes in pathways for cholinergic signaling,
which dampens inflammation by downregulating pro-inflammatory cytokines
(i.e., TNF-a, IL-1B and IL-6) [[129]46,[130]47]. Thus, DNA methylation
in COPD may not only affect physical manifestations of COPD, but also
contributes to the individuals’ ability to cope with psychosocial
stress of COPD.
Our analyses were limited by several factors. First, our study cohort
was small, and lacked significant sex/gender and ethnic diversity; thus
our findings may not be fully generalizable to patients in the
community. In addition the small sample size limited our investigation
of methylation patterns in smokers and ex-smokers separately. Second,
longitudinal effects of epigenetic disruptions could not be captured
due to the cross-sectional methodology of DISARM. Third, we were not
able to establish whether poor health status in COPD caused the
epigenetic disruptions or vice versa. Fourth, due to the invasive
nature of the bronchoscopy procedure, a replication cohort was not
available, and thus future efforts should aim to replicate our
findings. Fifth, bronchial brush samples, while mostly epithelial
cells, might have also included inflammatory cells, which may could
have impacted our results. However, previous research has shown that
epithelial cells are the main component of bronchial brush samples
[[131]4]; likewise, although our blood analysis were adjusted for cell
composition [[132]48] blood samples have a complex mixture of immune
cells, which varies across patients [[133]49] and therefore we were not
able to identify epigenetic differences for each specific cell
populations. In summary, there are epigenetic disruptions in blood and
airways associated with SGRQ scores, which may contribute to age-,
cancer- and neurological-related processes in COPD. Our findings
support the notion that the processes disrupted in the lung of COPD
patients could have systemic effects that may impact their quality of
life and symptoms, and that blood DNA methylation features are
sensitive indicators of similar changes in the airways. Together these
data suggest that blood DNA methylation patterns can be cultivated as
potential biomarkers of health status and outcomes of patients with
COPD.
Acknowledgments