Abstract
Background
Elevated blood pressure is an important risk factor for cardiovascular
disease and is also an important factor in global mortality. Military
pilots are at high risk of cardiovascular disease because they undergo
persistent noise, high mental tension, high altitude hypoxia, high
acceleration and high calorie diet. Hypertension is the leading cause
of cardiovascular disease in military pilots. In this study, we want to
identify key genes from peripheral blood cells of military pilots with
hypertension. Identification of these genes may help diagnose and
control hypertension and extend flight career for military pilots.
Methods
We use RNA sequencing technology, bioinformatics analysis and Western
blotting to identify key genes from peripheral blood cells of military
pilots with hypertension.
Results
Our study detected 121 up-regulated genes and 623 down-regulated genes
in the peripheral blood mononuclear cells (PBMCs) from hypertensive
military pilots. We have also identified 8 important genes (NME4,
PNPLA7, GGT5, PTGS2, IGF1R, NT5C2, ENTPD1 and PTEN), a number of gene
ontology categories and biological pathways that may be associated with
military pilot hypertension.
Conclusions
Our study may provide effective means for the prevention, diagnosis and
treatment of hypertension for military pilot and extend their flight
career.
Electronic supplementary material
The online version of this article (10.1186/s12920-018-0378-2) contains
supplementary material, which is available to authorized users.
Keywords: Hypertension, Military pilots, Peripheral blood cells, RNA
sequencing
Background
Elevated blood pressure (BP) is an important risk factor of
cardiovascular disease (CVD) and is also an important factor in global
mortality, leading to about 9.4 million deaths per year [[39]1]. The
morbidity and mortality of CVD are associated with degrees of increased
BP. For every increase of 20 mmHg in the systolic BP above 115 mmHg,
the incidence of CVD risk will double [[40]2]. In general, the
threshold of hypertension is set to systolic pressure of 140 mmHg
(150 mmHg for older adults) or diastolic pressure of 90 mmHg based on
BP measurement in quiescent condition [[41]3]. One-third American
adults over 18 years old of age have hypertension, and 54% of old
people (55- to 64-year-olds) have high BP. Among those over 75 years
old of age in the United States, nearly 80% have hypertension [[42]4].
Military pilots are at high risk of CVD because they are undergo
persistent noise, high mental tension, high altitude hypoxia, high
acceleration and high calorie diet [[43]5]. Hypertension is the leading
cause of CVD in military pilots. Although hypertension itself will not
cause sudden disability in flight, but it is a main risk factor for
disability in flight career and it is also one of the major reasons to
cause the pilot grounded [[44]6]. Wenzel et al. reported that the
incidence of hypertension in Brazilian Air Force is about 22% [[45]7].
Grossman et al. found 2.4% of the pilots had moderate or higher blood
pressure in a 7.5-year follow-up study of Israeli Air Force pilots
[[46]8]. The hypertension prevalence rate was 9.7% in Chinese Air Force
pilots, and the grounded rate of pilots was 21.7% among students in the
flight academy because of hypertension or increased blood pressure
[[47]9]. Essential hypertension is a disease caused by complex,
multifactorial and multigenic changes, and it is the result of both
gene regulation and environmental impact [[48]10]. In this study, we
use RNA sequencing technology, bioinformatics analysis and Western
blotting to identify key genes from peripheral blood cells of military
pilots with hypertension. Identification of these genes may help
diagnose and control hypertension and extend flight career for military
pilots.
Methods
Study subject
For RNA-Seq, six samples of peripheral blood cell from military pilot
(3 hypertensives and 3 normotensives) were collected. For quantitative
RT-PCR and Western blotting analysis, another 8 samples of peripheral
blood cell from fighter pilot (4 hypertensives and 4 normotensives)
were collected. All samples collected with the help of doctors from
Lintong Aviation Medical Evaluating and Training Center of Air Force,
Xi’an, China. The average systolic blood pressure (SBP) of these
hypertension pilots was above 160 mmHg and the average diastolic blood
pressure (DBP) was above 100 mmHg. The average SBP of these
normotensive pilots was below 135 mmHg and the average DBP was below
85 mmHg.
Peripheral blood mononuclear cells (PBMCs) isolation
Five milliliter whole blood collected was transferred to a 15 ml
sterile centrifuge tube, 5 ml phosphate buffer saline (PBS) was added
to dilute the whole blood. Five milliliter lymphocyte separation medium
was added to another 15 ml sterile centrifuge tube. The diluted whole
blood was transferred to the lymphocyte separation medium gently to
avoid mixing. Then the blood was centrifuged at 2000 rpm for 20 min,
room temperature. The white membrane cells of PBMSs were sucked into
another sterile 15 ml centrifuge tube. Add 5 ml PBS and centrifuge at
1000 rpm for 10 min. Aspirate supernatant and add 2 ml ACK buffer to
the tube, suspend the cells and keep standing for 5 min. Add 5 ml PBS
and centrifuge at 1000 rpm for 10 min. Aspirate the supernatant, and
the cells were quickly frozen in liquid nitrogen and used for further
analysis.
RNA sequencing
RNA sequencing was entrusted to Novel Bioinformatics Co., Ltd.,
Shanghai, China. Using high-throughput Life technologies Ion Proton
Sequencer, the transcript with poly(A)-containing RNA of Human were
analyzed.
Quality control
Fast - QC software
([49]http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was
employed to evaluate the overall quality of sequencing data.
Gene expression calculation
The expression of genes is mainly calculated by RPKM (Reads Per Kb per
Million reads) method. The formula is:
[MATH: RPKM=106CNL/103 :MATH]
RPKM is gene expression, C is the unique number of reads on the gene,
and N is the unique number of total reads in the reference gene, and L
is the base number of the gene coding region. RPKM method can eliminate
the effects of gene length and sequencing on the expression of genes,
which can be directly used to compare the gene expression differences
between different samples.
Principal component analysis
We applied the PCA analysis based the whole gene expression table and
the R script utilizing following package: MASS, evd, rgl and pvclust.
Command we used was described as followings:
R script:
dat < − read.table(“all.rpkm.exp.txt”,sep = “\t”, header = T).
colnameall = colnames(dat).
colname = colnames(dat[,2:length(colnameall)]).
dat.pca < −princomp(dat[2:length(colnameall)]).
summary(dat.pca).
plot<−plot3d(dat.pca$loadings[,1],dat.pca$loadings[,2],dat.pca$loadings
[,3],type = “s”,col. = col.,size = 0.8,xlab = “PC1”,ylab = “PC2”,zlab =
“PC3”).
texts3d(dat.pca$loadings[,1],dat.pca$loadings[,2]-0.02,dat.pca$loadings
[,3],texts = colname,font = 5).
GO analysis
The difference of gene analysis based on the database from BP, MF, CC
GO annotation in the three dimensions and all GOs were obtained. Each
GO significance level was obtained by using the Fisher test (P Value)
and so gene enrichment significant difference GOs were screened out
[[50]11, [51]12].
Pathway analysis
The differential expression genes filter out were annotated in KEGG
database ([52]http://www.genome.jp/kegg/) for Pathway annotations, and
all the Pathway Terms of different genes involved were got. The Pathway
of significance level is obtained by using the Fisher test (P Value),
and the significant Pathway Term of differential expression gene
enrichment was screened out.
Gene-act-network
The construction of gene interaction is to sort out the regulation of
all the genes, and through the construction of signal transduction
network, we could easily find the vein of gene signal transduction.
Based on KEGG database, we could get gene interactions. So, we
constructed gene and adjacency matrix using gene interactions
[[53]13–[54]17].
Western blotting
PBMCs were isolated as before. Total protein was extracted by using the
RIPA reagent (Beyotime, Shanghai, China). The sample protein
concentration was tested by measured by BCA Protein Assay reagents
(Thermo Scientific, Rockford, IL). After the protein electrophoresis,
the samples were transferred to the PVDF membrane, which was then
incubated for primary antibodies overnight at 4 °C, and then the
membrane was incubated for horseradish peroxidase (HRP)-conjugated
secondary antibodies for 2 h at room temperature. The primary
antibodies used were as follows: NT5C2 rabbit polyclonal antibody
(Proteintech, Wuhan, China), ENTPD1 rabbit polyclonal antibody
(Proteintech, Wuhan, China), GGT5 rabbit polyclonal antibody
(Proteintech, Wuhan, China), COX2 (PTGS2) rabbit polyclonal antibody
(Proteintech, Wuhan, China), PTEN rabbit polyclonal antibody
(Proteintech, Wuhan, China), IGF1R rabbit polyclonal antibody
(Proteintech, Wuhan, China), rabbit anti-NME4 (Bioss Antibodies,
Beijing, China), rabbit anti-PLCG2 (Bioss Antibodies, Beijing, China),
rabbit anti-PI3 Kinase p110 delta (PIK3D) (Bioss Antibodies, Beijing,
China), anti-PNPLA7 (Santa Cruz Biotechnology, Dallas, TX),
anti-β-actin (Santa Cruz Biotechnology, Dallas, TX). The membrane was
chemiluminescence using the chemiluminescent reagents (Millipore
Corporation, Billerica, MA) and image-forming system Tanon 4200 (Tanon
Science & Technology Co., Ltd., Shanghai, China).
Results
Overview of RNA sequencing data
We used Fast-QC online software
([55]http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to
assess the quality of sequencing results (Results were not shown). The
results showed that the sequencing data of all samples were qualified.
Total raw reads of these six samples were about 12–15 million. GC
content of these six samples was about 53%. The average reads mapped to
human genome sequence were about 1.28 ± 0.78 × 10^7reads (96% of the
total reads) in the six samples (Additional file [56]1: Table S1) and
about 92% of the total reads (1.24 ± 0.74 × 10^7 reads) were mapped to
human genome sequence uniquely. MapSplice was employed to map the
reads. According to the mapping results, about 1 × 10^7 reads were
mapped to the transcript exon, 7 × 10^6 reads were mapped to CDS,
2 × 10^6 reads were mapped to intron, 2 × 10^6 reads were mapped to the
UTR regions and the rest reads (about 5% or less) were mapped to
intergenic regions, TSS (transcription start site) and TES
(transcription end site) (Fig. [57]1a). We also detected the
distribution on chromosomes of these sample mapped reads (Fig. [58]1b).
The results showed that the most reads were aligned to chromosome 1
(about 10% or more) and the least reads were aligned to chromosome Y
(less than 0.2%).
Fig. 1.
[59]Fig. 1
[60]Open in a new tab
An overview of Reads Distribution and Chromosome Distribution of RNA
sequencing. a Reads number onto the regions of CDS, exons, intergenic
region (InterGenic), introns, transcription end site (TES),
transcription start site (TSS), 3’-UTR and 5’-UTR. b Distribution of
reads on chromosomes
Differential gene expression profiles between PBMCs from hypertensives and
normotensives military pilots and GO analysis
The six samples were screened for difference gene expression with 3:3.
The total number was 26, lower than the minimum standard of data
analysis. Two discrete samples (Con4 and Hyp3) were deleted according
to results of principal component analysis, and 744 differential genes
were acquired. This number was enough for further analysis, so we
adopted difference gene expression screened with 2:2.
To characterize the gene expression changes between hypertensives and
normotensives military pilots’ PBMCs, differentially expressed genes
were screened by the following standard: Log[2]FC > 0.585 or
Log[2]FC < − 0.585, FDR < 0.05 and P value < 0.05. We found 744
differentially expressed genes between hypertensives and normotensives
military pilots’ PBMCs. Of these genes, 121 genes were up-regulated in
hypertensives military pilots’ PBMCs and 623 genes were down-regulated.
PCA (principal components analysis) cluster analysis was used to
compare differential expression of these two groups. The differential
gene expression patterns of these samples were showed by gene thermal
map (Fig. [61]2). Gene ontology analysis was used to seek the functions
of these differentially expressed genes. The results showed that there
were 337 genes belonged to protein binding and 64 genes belonged to ATP
binding for molecular function (MF). There were 91 genes belonged to
signal transduction and 70 genes belonged to small molecule metabolic
process for biological process (BP). As for cellular component (CC),
there were 311 genes belonged to membrane and 248 genes belonged to
cytoplasm. Results from GO term analyzing showed that inflammatory
response, protein binding and phagolysosome were the most significant
for BP, MF and CC respectively (Fig. [62]3).
Fig. 2.
[63]Fig. 2
[64]Open in a new tab
Clustering of differentially expressed genes. a PCA cluster analysis of
these 6 samples. b Gene thermal map of differentially expressed genes
Fig. 3.
[65]Fig. 3
[66]Open in a new tab
Gene Ontology (GO) Analysis of differentially expressed genes. GO
analysis was annotated from three levels: BP, MF and CC.
-Log[10](P-value) was showed at abscissa axis and GO terms was showed
at longitudinal axis. P value < 0.05 for all significant GO terms. BP:
Biological Process; CC: Cellular Component; MF: Molecular Function
Pathways analysis of differential expression genes
The differential expression genes were involved in multiple GO, so we
constructed functional relation network with significant GO-Term
(p-value< 0.05) to reveal relationship between genes clearly based on
hierarchical structure of GO (Additional file [67]2: Figure S1). Of
these pathways, protein phosphorylation, toll-like receptor signaling
pathway and cell surface receptor signaling pathway were in the core
position (Additional file [68]2: Figure S1). Then, pathway-analysis was
carried out to detect significant and important pathways of these
differential expression genes. Influenza A and osteoclast
differentiation were the most significant (Fig. [69]4a). Also, the top
20 of pathway enrichment was displayed in Fig. [70]4b. P-Value and gene
number was indicated as circle size and color. Next, the pathways
interaction network was built to analysis deeply. The analysis results
showed that the most important pathways were apoptosis, Jak-STAT
signaling pathway, toll-like receptor signaling and cytokine-cytokine
receptor interaction (Additional file [71]3: Figure S2). Because these
four pathways are located at the center of the all significant pathways
and have the most arrowheads around, these four pathways are likely to
be most important in the elevated blood pressure of military pilots.
This result suggested that differential expression genes related to
apoptosis, Jak-STAT signaling pathway, toll-like receptor signaling and
cytokine-cytokine receptor interaction may have important role in the
occurrence and development of elevated blood pressure of military
pilots.
Fig. 4.
[72]Fig. 4
[73]Open in a new tab
Pathway enrichment analysis of differentially expressed genes based on
KEGG. a Histogram of pathway enrichment. -Log[10](P-value) was showed
at abscissa axis and pathway terms of KEGG was showed at longitudinal
axis. b Top 20 of pathway enrichment was displayed by different colors
and different sizes of the circle
Gene act network of differentially expressed genes
Although we got four important pathways related to elevated blood
pressure of military pilots, we know that one gene may be involved in
multiple signal transduction pathways at the same time. So next, we
built gene act network based on the relationships between the
differentially expressed genes including expression, binding,
inhibition, activation and compound. This analysis method can form the
corresponding regulation relationship between gene and gene and is
easier to find important related genes under the intervention measures.
By analysis the gene act network, we found that NME4, PNPLA7, GGT5,
PTGS2, IGF1R, PLCG2, NT5C2, ENTPD1, PIK3CD and PTEN these ten genes
were located at the center of the all significant genes and have the
most arrowheads around (Fig. [74]5). And also, these ten genes were
involved in apoptosis, Jak-STAT signaling pathway, toll-like receptor
signaling and cytokine-cytokine receptor interaction signaling pathway
previously mentioned. So Next, we will confirm changes of these genes
in military pilots’ PBMCs of hypertensives and normotensives with
Western blotting.
Fig. 5.
[75]Fig. 5
[76]Open in a new tab
Gene Act network analysis. Red circles represent up-regulated genes in
hypertension group; Green circles represent down-regulated genes in
hypertension group, arrows indicate the direction of regulation
Validation of representative differentially expressed genes by western
blotting
The expression of NME4, PNPLA7, GGT5, PTGS2, IGF1R, PLCG2, NT5C2,
ENTPD1, PIK3CD and PTEN from PBMCs of hypertensive and normotensive
military pilots were detected by Western blotting. The results from
Western blotting showed that NME4 and PNPLA7 these two genes were
up-regulated significantly and GGT5, PTGS2, IGF1R, NT5C2, ENTPD1 and
PTEN these six genes were down-regulated significantly in PBMCs of
military pilots with hypertension (Fig. [77]6). Although the expression
change of PLCG2 and PIK3CD was not significant between hypertensives
and normotensives, there was downward trend in PBMCs of hypertensive
military pilots (Fig. [78]6). The results indicated that the expression
change of NME4, PNPLA7, GGT5, PTGS2, IGF1R, NT5C2, ENTPD1 and PTEN
could be as sign of elevation of blood pressure for military pilots.
Fig. 6.
[79]Fig. 6
[80]Open in a new tab
Western blotting validation of relative expression levels of
representative differentially expressed genes. a Western blotting of 10
representative differentially expressed genes from 4 hypertensive (H1,
H2 H3 and H4) and 4 normotensive (N1, N2, N3, N4) military pilots. b
Statistics of relative expression of Western blotting. Bars = means ±
SD. ^*P < 0.05, ^**P < 0.01, NS, not significant, n = 4
Discussion
Military pilots are in a state of persistent noise, high mental
tension, high altitude hypoxia, high acceleration and their high
calorie diet, so hypertension is a very common disease in this group.
To identify the key genes that related to hypertension of military
pilots is very necessary for prevention, diagnosis and treatment of
this disorder. In this study, we used RNA sequencing of PBMCs to
identify differential gene expression profile between the hypertensive
and normotensive military pilots.
Six samples were sequenced and the difference gene expression was
compared with 3:3. Because two samples (Con4 and Hyp3) were discrete,
so we took them out and adopted different gene expression screened with
2:2. 121 up-regulated genes and 623 down-regulated genes were
identified as different expressed genes between hypertensive and
normotensive military pilots. We selected 10 important and significant
genes according to results of gene act network analysis. Western
blotting was employed to validate the expression change of the 10 genes
above. The results showed that expression change of NME4, PNPLA7, GGT5,
PTGS2, IGF1R, NT5C2, ENTPD1 and PTEN were consistent with the results
of RNA sequencing.
NME4 (also known as NDPK-D, Nm23-H4) belongs to the Nm23 family, which
includes 10 isoforms (NME1 to NME10) [[81]18]. The classical function
of NME4 as a group I isoform is its NDP kinase activity [[82]19].
PNPLA7 (also known as NTE-R1 or NRE) is a member of the PNPLAs family,
and its encoded protein is very conservative in mice, rats and human.
PNPLA7 plays an important role in the hydrolysis of triglycerides,
energy metabolism, lipid formation and adipocyte differentiation
[[83]20]. Up-regulation of PNPLA7 indicates elevated blood lipids,
which has some correlation with hypertension.
The main expression of Gamma-glutamyl transferase 5 (GGT5) is on the
surface of cell membrane, and the role of GGT5 is to hydrolyze the
gamma-glutamyl bond glutathione [[84]21]. There is no phenotypic
abnormality in GGT5 knockout mice under normal conditions. But GGT5
gene knockout mice were unable to metabolize LTC4, resulting in
diminished potential of neutrophils infiltrating into the peritoneum.
The expression and function of GGT5 in human are rarely reported
[[85]22]. PTGS2(also known as COX-2) could promote carcinogenesis and
metastasis of multiple types of tumors. Expression of PTGS2 could be
dramatically up-regulated by high levels of noise exposure and high
altitude hypoxia [[86]23, [87]24]. Park et al. reported that high fat
diet could reduce PTGS2 expression [[88]25], indicating that expression
change of PTGS2 may be caused by noise exposure, high altitude hypoxia
and high fat diet. IGFR is a member of the receptor tyrosine family and
can form homodimers with insulin receptor (InsR) to identify and bind
to the ligand of insulin receptor IGF1 and IGF2 [[89]26]. Heterozygous
deficiency of Igf1r reduced postnatal growth and develop age-dependent
insulin resistance. Old-aged Igf1r^+/− mice had increased adiposity and
exhibited increased adipogenesis [[90]27], indicated that reduced
expression of IGF1R may have a correlation with hypertension. NT5C2
plays an important role in purine metabolism. Some papers reported that
there were some relationships between somatic mutations of NT5C2 and
T-acute lymphoblastic leukemias (T-ALL) [[91]28, [92]29]. ENTPD1 (also
known as CD39) is a plasma membrane protein and its role is to
hydrolyze extracellular ATP and ADP to AMP. Helenius et al. reported
that the expression of ENTPD1 was significantly reduced in small
arterial endothelial cells in patients with pulmonary arterial
hypertension (PAH). The attenuation function of ENTPD1 is closely
related to vascular dysfunction, suggested that ENTPD1 may be a novel
target for PAH therapy [[93]30]. Their results strongly suggested that
down-regulation of ENTPD1 may be a sign of hypertension, consistent
with our results. PTEN signaling pathway is one of the most important
signaling pathways that regulate individual development, and
participate in the process of cell proliferation, differentiation,
aging and apoptosis. Schwerd et al. reported that mutation of PTEN led
severe macrocephaly and mild intellectual disability in adolescent
[[94]31]. Skalska-Sadowska et al. reported that mutations of PTEN
induced T-ALL [[95]32]. These results suggested that abnormal
expression of PTEN could lead nervous system abnormalities and
hematologic disease, maybe have some relationship with hypertension.
Some of the above eight genes are associated with a high-fat diet, some
are associated with noise and high-altitude hypoxia, some may be
associated with high mental tension. We hypothesized that the
expression change of these genes may affect the level of insulin-like
growth factor and small vessel remodeling to induce hypertension under
the long-term impact of flight environment. Therefore, we should pay
attention to the expression changes of these eight genes in the
prevention and control of military pilots’ hypertension. Early
prevention and early treatment according to the expression change of
these eight genes, may extend flight career of military pilots.
At last, we acknowledge that the lack of non-pilot controls is a
limitation of the study. The eight differential genes we identified may
not be pilots specific. Therefore, our results may lead to some errors
in the prevention, diagnosis and treatment of pilot hypertension. But
our study still has good guidance for the prevention, diagnosis, and
treatment of hypertension in pilots, because the samples used in RNA
sequencing and Western blotting experiments in this study are all from
military pilots. We will employ non-pilot controls including
hypertensives and normotensives to identify differential genes more
accurate in the future.
Conclusions
In summary, this study detected gene expression difference in PBMCs
between military pilot hypertension and normotensives. We have also
identified 8 important genes (NME4, PNPLA7, GGT5, PTGS2, IGF1R, NT5C2,
ENTPD1 and PTEN), a few GO categories and biological pathways that may
be associated with the military pilot hypertension. Our study may
provide effective means for the prevention, diagnosis and treatment
hypertension of military pilot, extend their flight career.
Additional files
[96]Additional file 1:^ (62.8KB, pptx)
Table S1. Statistics of raw and mapped reads from RNA-seq analysis of
PBMCs from hypertensives (Hyp) and normotensives (Con) military pilot.
(PPTX 62 kb)
[97]Additional file 2:^ (664.4KB, pptx)
Figure S1. Relationship between genes based on hierarchical structure
of GO. (PPTX 664 kb)
[98]Additional file 3:^ (496.9KB, pptx)
Figure S2. Pathways interaction network analysis. (PPTX 496 kb)
Acknowledgements