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New (arene)ruthenium(II) complexes of 4‑aryl‑4H‑naphthopyrans with anticancer and anti-vascular activities.
Protein expression profile changes of
lung tissue in patients with pulmonary
hypertension
Min Wu, Yijin Wu, Jinsong Huang, Yueheng Wu, Hongmei Wu, Benyuan Jiang
and Jian Zhuang
Department of Cardiac Surgery, Guangdong Provincial People’s Hospital, Guangzhou City, Guangdong
Province, China
ABSTRACT
Submitted 21 August 2019
Accepted 4 November 2019
Published 31 January 2020
Corresponding authors
Min Wu, wumin0011@gdph.org.cn
Jian Zhuang,
zhuangjian5413@tom.com
Academic editor
Vladimir Uversky
Additional Information and
Declarations can be found on
page 14
DOI 10.7717/peerj.8153
Copyright
2020 Wu et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Background. Pulmonary hypertension occurs in approximately 1% of the global
population, and the prognosis for such patients may be poor. However, the mechanisms
underlying the development of this disease remain unclear. Thus, understanding the
development of pulmonary hypertension and finding new therapeutic targets and
approaches are important for improved clinical outcomes.
Methods. Lung tissue specimens were collected from six patients with atrial septal defect
and pulmonary hypertension (all women, with a mean age of 46.5 ± 4.7 years, and their
condition could not be corrected with an internal medical occlusion device) and from
nine control patients with lung cancer who underwent lobectomy (six men and three
women, with a mean age of 56.7 ± 1.7 years). Isobaric tags for relative and absolute
quantitation and liquid chromatography tandem mass spectrometry analyses were used
to detect protein expression levels.
Results. We found 74 significantly upregulated and 88 significantly downregulated
differentially expressed proteins between control and pulmonary hypertensive lung
tissue specimens. Gene ontology analyses identified the top 20 terms in all three
categories, that is, biological process, cellular component, and molecular function.
Kyoto Encyclopedia of Genes and Genomes and protein–protein interaction analyses
determined the top 10 signaling pathways and found that the six hub proteins associated
with the differentially expressed upregulated proteins (PRKAA1, DHPR, ACTB,
desmin, ACTG1, and ITGA1) were all involved in hypertrophic cardiomyopathy,
arrhythmogenic right ventricular cardiomyopathy, and dilated cardiomyopathy.
Conclusion. Our results identified protein expression profile changes in lung tissue derived from patients with pulmonary hypertension, providing potential new biomarkers
for clinical diagnosis and prognosis for patients with pulmonary hypertension and
offering candidate protein targets for future therapeutic drug development.
Subjects Bioinformatics, Cardiology, Respiratory Medicine
Keywords Pulmonary hypertension, Proteomics, LC–MS/MS, iTRAQ, PRKAA1, DHPR
INTRODUCTION
Pulmonary hypertension is a chronic, persistent complex disease involving many lesions
(Kim & George, 2019). The incidence of pulmonary hypertension is approximately 1%
of the global population (Hoeper et al., 2016). Epidemiological surveys in Europe and
How to cite this article Wu M, Wu Y, Huang J, Wu Y, Wu H, Jiang B, Zhuang J. 2020. Protein expression profile changes of lung tissue
in patients with pulmonary hypertension. PeerJ 8:e8153 http://doi.org/10.7717/peerj.8153
the United States show that idiopathic pulmonary hypertension is most common in
women and that familial pulmonary hypertension is associated with genes (Franco, Ryan
& McLaughlin, 2019; McGoon et al., 2013). Pulmonary hypertension is a chromosomal
dominant genetic disease mainly attributable to bone morphogenetic protein receptor-2
mutations (Harper et al., 2019). Other types of pulmonary hypertension are often associated
with a variety of diseases, including congenital heart disease, chronic obstructive pulmonary
disease, connective tissue disease, drug and toxin effects, HIV/AIDS, hemoglobinopathy,
and coagulation disorders (Basyal, Jarrett & Barnett, 2019; Kim & George, 2019; Mitra
et al., 2018; Zhang et al., 2019). Pulmonary hypertension can also be related to certain
atmospheric conditions, such as hypoxia observed at high altitude. Without effective
treatment, the prognosis for patients with pulmonary hypertensive is generally poor. The
annual mortality rate of such patients is approximately 15% (Thenappan et al., 2007). Poor
cardiopulmonary function, low mobility, increased right atrial pressure, progressive right
ventricular failure, low cardiac output, increased brain natriuretic peptide, and progression
of connective tissue disease are all predictors of poor prognosis (Fukuda et al., 2019).
Recent studies have revealed several new candidate targets and approaches for
development of future pulmonary hypertension treatment. Zhang et al. (2019) have
suggested that autophagy of pulmonary artery endothelial and smooth muscle cells
may induce dysfunction of the pulmonary arteries and eventually lead to pulmonary
hypertension (Zhang et al., 2019). Lambert et al. (2018) have reported that ion channel
activity changes in pulmonary artery endothelial and smooth muscle cells may
pathophysiologically contribute to the development of pulmonary hypertension (Lambert et
al., 2018). Additionally, immune system dysfunction and inflammation may be linked with
pulmonary arterial hypertension pathogenesis (Goldenberg & Steinberg, 2019). Therefore,
it is important to find new and effective targets to treat pulmonary hypertension.
Isobaric tags for relative and absolute quantitation (iTRAQ) in combination with
liquid chromatography tandem mass spectrometry (LC–MS/MS) are powerful tools
for identifying protein expression levels of the various types of proteins that are in a
specimen. In the present study, we obtained pulmonary tissue specimens from patients
undergoing surgical pulmonary procedures and used the iTRAQ and LC–MS/MS methods
to identify proteins that were differentially expressed in the pulmonary tissue of patients
with pulmonary hypertension versus those from patients without this disease. Using
bioinformatics analysis, we assessed those differentially expressed proteins (DEPs) to
determine potential key proteins and their related signaling pathways that may be associated
with the development of pulmonary hypertension.
MATERIALS & METHODS
Clinical specimens and patient enrollment
Our research plan and an informed patient consent form for the present study were
submitted to the Medical Research Ethics Committee of Guangdong Provincial People’s
Hospital. The Medical Ethics Committee reviewed and approved our submitted proposal,
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ethical approval number Guangdong Medical Ethics 2016220H (R1)/GDREC2016220H
(R1). All patients signed the written informed consent form prior to being included in the
study.
Patients were enrolled in strict accordance with the following inclusion and exclusion
criteria, as specified in the experimental protocol. For the experimental group, the included
patients had been diagnosed as having pulmonary hypertension with an atrial septal defect
that could not be corrected by internal closure. In the resting state, with the patient lying
so that the left atrium was in a horizontal position and the measurement determined at a
distance midway between the middle of the sternum and the bedside, the average pressure
of the pulmonary artery measured by a catheter located in the right ventricle of the heart
was ≥25 mmHg. For the control group, the included patients did not have pulmonary
hypertension and had received a diagnosis of central lung cancer requiring lobectomy, but
the cancer tissue had not invaded the peripheral lung tissue used in this study.
iTRAQ labeling and fractionation by cation exchange
chromatography
The 8-plex iTRAQ labeling method was performed as previously described (Ding et al.,
2019; Liu et al., 2019). The reagents were used to label protein peptides from each group
multiplex kit (ABI, Foster City, CA, USA) (isobaric tags 113, 114, and 116 for the control
group, and isobaric tags 118, 119, and 121 for the pulmonary hypertension group). For
each specimen, protein (200 µg) was precipitated with acetone at −20 ◦ C overnight. After
centrifugation for 10 min, the protein pellet was dissolved in 60 µL of iTRAQ dissolution
buffer (Applied Biosystems). The iTRAQ labeling reagents were added to the corresponding
peptide specimen to react at room temperature for 1 h. After the reaction for the labeling was
stopped and an extraction efficiency test was performed, the specimen was ZipTip desalted
and subjected to a matrix-assisted laser desorption ionization procedure. Each group was
pooled and then vacuum-dried. Each pooled specimen containing mixed peptides was
lyophilized and dissolved in solution A (2% acetonitrile and 20 mM ammonium formate,
pH 10). This solution was loaded on a reverse-phase column (Luna C18, 4.6 × 150 mm;
Phenomenex; Torrance, CA, USA) and eluted using a step linear elution program at a flow
rate of 0.8 mL/min: 0%–10% buffer B (500 mM KCl, 10 mM KH2 PO4 in 25% acetonitrile,
pH 2.7) for 10 min, 10%–20% buffer B for 25 min, 20%–45% buffer B for 5 min, and
50%–100% buffer B for 5 min. The eluent fractions were collected and centrifuged for 5–45
min. The fractions were finally combined into six pools and desalted using C18 Cartridges
(Empore standard density Solid Phase Extraction C18 Cartridges, bed I.D. seven mm, three
mL volume; Sigma; St. Louis, MO, USA).
LC–MS/MS analysis
We performed MS using a TripleTOF 5600 system (AB SCIEX) combined with a nanoliter
spray III ion source (AB SCIEX, USA). The spray voltage was set at 2.5 kV; the air curtain
pressure, at 30 psi; the atomization pressure at 5 psi; and the heater temperature, at 150 ◦ C.
The scanning mode used was information-dependent acquisition. The scan time of the
first time-of-flight (TOF)–MS single image was 250 ms. Each information-dependent
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Figure 1 Flowchart of the data analysis procedure.
Full-size
DOI: 10.7717/peerj.8153/fig-1
acquisition cycle collected 35 secondary TOF–MS images. Each cycle time was fixed at 2.5
s. The dynamic exclusion was set to 18 s, which was approximately equal to half the width
of the chromatogram.
Sequence database search and data analysis
The data were processed according to the flowchart shown in Fig. 1. After the data were
collected, we used Protein Pilot Software v. 5.0 (AB SCIEX, USA) for the analysis. Human
proteome databases containing reviewed Uniprot sequences were used to perform peptide
identification. Decoys for the database search were generated with the revert function. We
chose the following options to identify the proteins: cysteine alkylation = iodoacetamide;
enzyme = trypsin; search effort = thorough. The proteins that showed a relative upregulated
or downregulated fold change of at least 1.2 (in both replicates) and that were significantly
different between the groups at P <0.05 were determined to be differentially expressed
between the groups.
Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway enrichment analyses
We performed GO analyses using Metascape, a web-based resource for gene and protein
annotation, visualization, and integration discovery (http://metascape.org) (Fang et al.,
2019; Soonthornvacharin et al., 2017). KEGG pathway analyses of the DEPs were performed
using the KOBAS online analysis database (http://kobas.cbi.pku.edu.cn/) (Kanehisa &
Goto, 2000). A two-sided P <0.05 was considered statistically significant.
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Table 1 Results of protein identification analyses. Using an Unused ProtScore cutoff >1.3 and peptides ≥1, we obtained 2,953 proteins.
Unused ProtScore
(Conf) cutoff
Proteins
detected, No.
Proteins before
grouping, No.
Distinct
peptides, No.
Spectra
identified, No.
% of Total
spectra
>2.0 (99)
2,502
8,613
35,550
119,806
46.2
>1.3 (95)
3,118
12,147
36,406
121,271
46.8
>0.47 (66)
3,251
12,682
36,614
121,540
46.9
Cutoff >0.05 (10%) applied
3,497
14,210
37,010
122,026
47.1
Protein–protein interaction (PPI) network construction and module
analyses
PPI analysis was used to assess the functions associated with the DEPs and to determine
the general organizational principles of the functional cellular networks. The functional
relationships between proteins were identified using the Search Tool for the Retrieval of
Interacting Genes (STRING; http://string.embl.de/) (Fang et al., 2019; Von Mering et al.,
2003). The PPI networks associated with the respective DEPs were constructed to predict
the interaction of those proteins.
RESULTS
Specimen collection and patient characteristics
From April to September 2016, we collected lung tissue specimens from six patients with
atrial septal defect and pulmonary hypertension. The patients were all women, with a
mean (SD) age of 46.5 ± 4.7 years, and their condition could not be corrected with an
internal medical occlusion device. For the control group, nine patients with lung cancer
who underwent lobectomy were enrolled: six men and three women, with a mean (SD)
age of 56.7 ± 1.7 years. Among the patients with pulmonary hypertension, the mean
pulmonary systolic pressure was 66.7 ± 5.5 mmHg; mean pulmonary artery diastolic
pressure, 23.2 ± 1.6 mmHg; mean pulmonary artery pressure, 39.7 ± 3.4 mmHg; mean
pulmonary artery resistance, 3.27 ± 0.28 Wood units; pulmonary oxygen saturation,
87.3% ± 0.84%; and mean patient body surface, 1.52 ± 0.06 m2 .
Identification of DEPs in pulmonary tissue
After performing the quantitative analyses with Protein Pilot Software, we identified 2,953
proteins using a detection protein threshold (Unused ProtScore [Conf]) cutoff of >1.3;
95% confidential interval) (Table 1), and we obtained the protein expression fold changes
between the control specimens and the pulmonary hypertensive lung tissue specimens. The
data indicated marked protein expression profile changes between the control lung tissue
and the pulmonary hypertensive lung tissue. Using our selection criteria, we identified 74
significantly upregulated DEPs, including integrin subunit α1 (ITGA1) and the voltagegated calcium (Ca2+ ) channel α2/ δ1 subunit, and 88 significantly downregulated DEPs,
including Rho-GDP dissociation inhibitor 2 (Table 2, Fig. 2).
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Table 2 Fold changes of key proteins in pulmonary hypertensive lung tissue.
Protein ID
(UniProt)
Gene symbol
Fold change
P54289
CACNA2D1
1.246
P17661
DES
1.257
Q13131
PRKAA1
1.794
P60709
ACTB
1.215
P56199
ITGA1
1.209
P63261
ACTG1
1.215
P52566
ARHGDIB
0.743
Figure 1 Differential protein expression. (A) Volcano plot with red dots on the right-hand side indicating upregulation, red points on the left-hand side indicating downregulation, and black dots indicating no
significant change in protein expression levels based on the criteria of an absolute log2 fold change (FC)
>1.2 and P < 0.05. (B) Bar graph indicating 74 upregulated and 88 downregulated proteins. DEP indicates differentially expressed protein.
Full-size DOI: 10.7717/peerj.8153/fig-2
GO functional enrichment analysis
GO analysis is used to hierarchically classify genes or gene products into categories organized
in an ontology. The analysis is based on three categories: (1) molecular function, to describe
the molecular activity of a gene; (2) biological process, to characterize the larger cellular
or physiological role of the gene; and (3) cellular component, to indicate the location of
the gene or gene product in the cell. We used Metascape to enrich all identified proteins.
Among the top 20 terms in the biological process category, the highest percentages of the
proteins were associated with the term single-organism process (Figs. 3 and 4) (n = 119
proteins; with the top 3 upregulated proteins in this process being LDLRAP1, FTL, and
CHUK). The maximum levels of the expressed proteins this category were detected for
the term protein complex subunit organization (n = 27 proteins; with the top 3 upregulated
proteins being SLC6A4, FRYL, and HIST2H3A). Among the top 20 terms in the GO
category cellular component, the highest percentages of proteins were associated with
the term cytoplasm (n = 104 proteins, with the top 3 upregulated proteins for this term
being LDLRAP1, FTL and CHUK. The maximum levels of the expressed proteins in this
category were in the term major histocompatibility complex (MHC), which is a group of
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Figure 3 Enrichment of the gene ontology functional terms in the top 20 entries of the three categories
of biological process, cellular component, and molecular function for highly expressed proteins. The
entries in each category are sorted from left to right according to their −log P value. The more significant,
and the number of proteins corresponding to each item, can be viewed from the left to right vertical axes.
The number of proteins is a percentage of the total number of submitted proteins. For the category biological process, the full name of ‘‘antigen processing and presentation of . . . ’’ is antigen processing and
presentation of peptide or polysaccharide antigen via MHC class II.
Full-size DOI: 10.7717/peerj.8153/fig-3
proteins located on the cell surface (n = 9 proteins, with the top 2 upregulated proteins
being HLA-A and HLA-DPA1). In the top 20 terms of the Go category molecular function,
the highest percentages of proteins were associated with the term binding (113 proteins,
with the top 3 upregulated proteins being LDLRAP1, FTL and CHUK), and the maximum
levels of the expressed proteins in this category were for the term ferric iron binding (with
the top 2 upregulated proteins being FTL and FTH1). The GO functional enrichment
analysis assessing the distributions of the proteins with the greatest fold changes in the
biological process, molecular function, and cellular component categories indicated that
the proteins associated with the terms organism process, protein binding, and extracellular
vesicle, respectively, were markedly changed.
KEGG signaling pathway analysis and PPI network construction
To identify the functions associated with the DEPs, we used the KOBAS online analysis
tool to determine the KEGG signaling pathways, and we used the STRING online analysis
tool to construct the PPI networks. Through KEGG signaling pathway analysis, the
gene content in the provided genome is compared with the information in the KEGG
pathway database to clarify which pathways and associated functions link to the genes
in the genome. The PPI network indicates physical contacts between two or more
proteins resulting from biochemical events controlled by electrostatic forces, including
hydrophobic effects. The top 10 KEGG signaling pathways were enriched, and the PPI
networks were constructed (Figs. 5–9). We found that the top 10 signaling pathways
included hypertrophic cardiomyopathy, systemic lupus erythematosus, arrhythmogenic
right ventricular cardiomyopathy, dilated cardiomyopathy, pathogenic Escherichia coli
infection, viral myocarditis, phagosome, alcoholism, cardiac muscle contraction, and
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Figure 4 Maximum level corresponding to the top 20 proteins in the gene ontology enrichment categories of biological process (A), cellular component (B), and molecular function (C), and the percentages of enriched proteins. The ordinate is arranged from low to high according to the maximum level,
and each level is arranged by P value. The abscissa is the percentages of the enriched proteins. The number
at the end of each bar is the number of proteins enriched in the biological process, cellular component, or
molecular function gene ontology category. In A, the full name of ‘‘antigen processing and presentation of
. . . ’’ is antigen processing and presentation of peptide or polysaccharide antigen via MHC class II.
Full-size DOI: 10.7717/peerj.8153/fig-4
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Figure 5 Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. Top 10 KEGG signaling pathways (A) and KEGG classification histogram (B). In B, all enriched pathways with significant
differences in P values are shown. The ordinate is the specific pathway classification and name. On the
right, the number of associated genes, P values, and their corresponding KEGG classifications are shown.
The abscissa gives the percentages of the genes.
Full-size DOI: 10.7717/peerj.8153/fig-5
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Figure 6 Changes in the hypertrophic cardiomyopathy signaling pathway in lung tissue of patients
with pulmonary hypertension. Upregulated proteins are highlighted in red boxes.
Full-size DOI: 10.7717/peerj.8153/fig-6
ribosomes, which are predominantly associated with human diseases (Fig. 5). Some of the
DEPs crossed several different signaling pathways. For example, the proteins highlighted
in red boxes in Figs. 6, 7 and 8 including DHPR, ACTB, desmin, ACTG1, PRKAA1
and ITGA1, were all involved in hypertrophic cardiomyopathy, arrhythmogenic right
ventricular cardiomyopathy, and dilated cardiomyopathy (Table 2). The results of the
KEGG pathway enrichment and PPI network construction analyses indicated that many
of the upregulated proteins (shown as brown dots in Fig. 9) were involved in hypertrophic
cardiomyopathy, By contrast, many of the downregulated proteins (shown as green dots
in Fig. 9) were related to ribosome function. Taken together, our results suggest that the
heart is a major organ impaired by pulmonary hypertension.
DISCUSSION
In the present study, we used clinical specimens obtained from patients undergoing surgical
procedures, and we used the iTRAQ method combined with LC–MS/MS to identify the
key proteins and signaling pathways associated with the development of pulmonary
hypertension. Our primary findings were as follows: (1) We identified 2953 proteins,
including 74 significantly upregulated DEPs and 88 significantly downregulated DEPs,
between control and pulmonary hypertensive lung tissues; (2) GO analysis elucidated
the top 20 terms associated with categories of biological process, cellular component, and
molecular function; (3) KEGG and PPI analyses identified the top 10 signaling pathways and
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Figure 7 Changes in the arrhythmogenic right ventricular cardiomyopathy signaling pathway in lung
tissue of patients with pulmonary hypertension. Upregulated proteins are highlighted in red boxes.
Full-size DOI: 10.7717/peerj.8153/fig-7
six hub proteins, including PRKAA1, DHPR, ACTB, desmin, ACTG1 and ITGA1. Taken
together, our study revealed protein expression profile changes in lung tissue of patients
with pulmonary hypertension, providing a deeper understanding of the development of
pulmonary hypertension and suggesting several potential targets for the development of
new drugs in the treatment of pulmonary hypertension.
In the present study, we detected a high expression of PRKAA1, which encodes 50 -AMPactivated protein kinase catalytic subunit alpha-1 (AMPKα1). AMPKα1 is an enzyme
involved in the pathological changes of smooth muscle cells in vessels of patients with
pulmonary hypertension. AMPKα1 can maintain smooth muscle cells and their survival in
an anoxic environment, and inhibition of AMPKα1 leads to hypoxia-induced activation of
autophagy. In a hypoxic environment, AMPK is activated and phosphorylated in smooth
muscle cells. Under hypoxic conditions, lactate dehydrogenase activity is increased after
AMPK activity is inhibited by the AMPK antagonist compound C, leading to the induction
of apoptosis in smooth muscle cells (Ibe et al., 2013). Evans et al. (2015) have suggested
that the levels of AMPK and liver kinase B1 (LKB1) are upregulated when mitochondria
sense a change in the oxygen supply. These upregulated levels cause a sustained blood
vessel contraction through an LKB1–AMPK signaling pathway and result in continuous
pulmonary hypertension. AMPK is activated by an increase in the ratio of ADP to ATP and
rapidly amplifies the signaling pathway in synergy with LKB1, maintaining the intracellular
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Figure 8 Changes in the viral myocarditis signaling pathway in lung tissue of patients with pulmonary
hypertension. Upregulated proteins are highlighted in red boxes.
Full-size DOI: 10.7717/peerj.8153/fig-8
Figure 9 Protein–protein interaction networks in the Kyoto Encyclopedia of Genes and Genomes
pathways that are ranked as the top 10 regulatory pathways.
Full-size DOI: 10.7717/peerj.8153/fig-9
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ATP concentration by catabolism and inhibition of unnecessary ATP depletion. Therefore,
AMPK regulates the mitochondrial energy balance and maintains the energy supply of
smooth muscle cells during hypoxia to preserve normal physiological functions (Evans et
al., 2015). In the present study, the expression of AMPKα1 was significantly increased in
the lung tissue of patients with pulmonary hypertension. This finding suggests that AMPK
may be a potential target in the treatment of pulmonary hypertension.
We also found an upregulation of ITGA1, which is involved in the regulation of
phosphorylation and the biological processes of essential metabolic processes. The integrins
α1–5, α7–8, αv, β1, β3, and β4 have been detected on pulmonary vessels. Umesh et
al. used a mouse model of chronic hypoxic pulmonary hypertension and performed
immunohistochemical staining of pulmonary artery smooth cells to find that the level
of ITGA1 expression on the cell surface was significantly increased, whereas that of
integrin subunit α5 was significantly decreased (Umesh et al., 2011; Umesh et al., 2006).
These expression level changes were mainly concentrated on the smooth muscle cells of
peripheral pulmonary arterioles and were associated with Ca2+ flow in smooth muscle cells
(Umesh et al., 2011). Eventually, a Ca2+ concentration change in vascular smooth muscle
cells affects blood vessel contraction and blood pressure. In the present study, we also found
an increase in ITGA1 in the lung tissue of patients with pulmonary hypertension. Thus,
the increased level of this integrin may be a key reason for the development of pulmonary
hypertension.
Smooth muscle cell contraction is regulated by intracellular Ca2+ and Rho kinase
signaling pathways (Berridge, 2008). Ca2+ channels are involved in the pathological
processes associated with pulmonary hypertension. Thus, Ca2+ channel antagonists have
been used in the early stages of clinical treatment of pulmonary hypertension, and some
patients have achieved some therapeutic effects (Kennedy, Michael & Summer, 1985; Packer,
1985; Packer, Medina & Yushak, 1984; Rubin, 1985). In the present study, we found that the
voltage-gated Ca2+ channel subunit α2/δ1 encoded by CACN2D1 significantly increased in
the lung tissue of patients with pulmonary hypertension. Moreover, we also found that the
ARHGDIB-encoded Rho-GDP dissociation inhibitor 2 protein is downregulated (Table 2).
Rho-GDP dissociation inhibitor 2 can inhibit the dissociation of GDP from Rho protein,
thereby regulating the rate of the GDP/GTP exchange reaction and inhibiting smooth
muscle cell contraction (Adra et al., 1993; Scherle, Behrens & Staudt, 1993). Therefore, an
increase in voltage-gated Ca2+ channels and a decrease in Rho-GDP dissociation inhibitor
2 levels will eventually induce smooth muscle cell contraction, increase vascular resistance,
and participate in the pathogenesis of pulmonary hypertension.
In the present study, we also found that three muscle contraction–related proteins,
including β-actin encoded by ACTB, γ -actin encoded by ACTG1, and desmin encoded
by DES, were significantly increased. Beta-actin is one of two nonmuscle cytoskeletal
actins, and it is involved in cell motility, structure, and integrity (Gunning et al., 2015).
Alpha-actins are a major constituent of the contractile apparatus (Yonemura, 2017). In
adult striated muscle cells, γ -actin localizes to Z-disks and costamere structures and
is involved in force transduction and transmission in muscle cells (Nakata, Nishina &
Yorifuji, 2001; Papponen et al., 2009). Desmin is a type III intermediate filament in cardiac
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muscle, skeletal muscle, and smooth muscle tissue. It integrates the sarcolemma, Z disk,
and nuclear membrane in sarcomeres and regulates sarcomere architecture (Brodehl,
Gaertner-Rommel & Milting, 2018; Sequeira et al., 2014). Therefore, these upregulated
muscle contraction–related proteins may enhance vascular smooth muscle cell contraction
to increase vascular resistance and may thus be involved in the pathogenesis of pulmonary
hypertension.
CONCLUSIONS
We used the iTRAQ method to identify 2953 proteins, including 74 significantly upregulated
DEPs and 88 significantly downregulated DEPs between control and pulmonary
hypertensive lung tissues. Through bioinformatics analyses, we found six increased key
proteins, including PRKAA1, DHPR, ACTB, desmin, ACTG1, and ITGA1. The findings of
the present study provide potential new biomarkers for clinical diagnosis and prognosis
for pulmonary hypertension and candidate protein targets for drug development.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by grants from the Guangdong Project of Science and Technology
(2017A070701013, 2017B090904034, 2017B030314109). The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Guangdong Project of Science and Technology: 2017A070701013, 2017B090904034,
2017B030314109.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
• Min Wu conceived and designed the experiments, performed the experiments, analyzed
the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and
approved the final draft.
• Yijin Wu and Jinsong Huang performed the experiments, analyzed the data, authored
or reviewed drafts of the paper, and approved the final draft.
• Yueheng Wu analyzed the data, prepared figures and/or tables, authored or reviewed
drafts of the paper, and approved the final draft.
• Hongmei Wu analyzed the data, prepared figures and/or tables, and approved the final
draft.
• Benyuan Jiang conceived and designed the experiments, prepared figures and/or tables,
and approved the final draft.
• Jian Zhuang conceived and designed the experiments, prepared figures and/or tables,
authored or reviewed drafts of the paper, and approved the final draft.
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Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
The Medical Research Ethics Committee of Guangdong Provincial People’s
Hospital (Guangdong Academy of Medical Sciences) approved this research (016220H
(R1)/GDREC2016220H (R1)).
Data Availability
The following information was supplied regarding data availability:
The raw measurements are available in the Supplementary Files.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.8153#supplemental-information.
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