Abstract
Pulmonary arterial endothelial cells (PAEC) are mechanistically linked
to origins of pulmonary arterial hypertension (PAH). Here, global
proteomics and phosphoproteomics of PAEC from PAH (n = 4) and healthy
lungs (n = 5) were performed using LC-MS/MS to confirm known pathways
and identify new areas of investigation in PAH. Among PAH and control
cells, 170 proteins and 240 phosphopeptides were differentially
expressed; of these, 45 proteins and 18 phosphopeptides were located in
the mitochondria. Pathologic pathways were identified with integrative
bioinformatics and human protein-protein interactome network analyses,
then confirmed with targeted proteomics in PAH PAEC and non-targeted
metabolomics and targeted high-performance liquid chromatography of
metabolites in plasma from PAH patients (n = 30) and healthy controls
(n = 12). Dysregulated pathways in PAH include accelerated one carbon
metabolism, abnormal tricarboxylic acid (TCA) cycle flux and glutamate
metabolism, dysfunctional arginine and nitric oxide pathways, and
increased oxidative stress. Functional studies in cells confirmed
abnormalities in glucose metabolism, mitochondrial oxygen consumption,
and production of reactive oxygen species in PAH. Altogether, the
findings indicate that PAH is typified by changes in metabolic pathways
that are primarily found in mitochondria.
Subject terms: Respiratory tract diseases, Mechanisms of disease,
Protein-protein interaction networks
Introduction
Pulmonary arterial hypertension (PAH) is a fatal disease characterized
by impaired regulation of pulmonary hemodynamics and vascular growth.
Endothelial cell dysfunction in the arteries of PAH lungs is
mechanistically linked to the pathobiology of PAH^[50]1–[51]5. For
example, the endothelial production of the critical vasodilator nitric
oxide (NO) is deficient in PAH^[52]4,[53]6–[54]9 due to phosphorylation
inactivation of endothelial NO synthase (NOS3)^[55]10. Pulmonary
arterial endothelial cells (PAEC) derived from human PAH lungs continue
to exhibit a pathologic phenotype, including decreased NO production.
The cells also manifest an abnormal metabolic phenotype that is
characterized by decreased mitochondrial respiration, significantly
higher glycolytic rate, apoptosis resistance, increased cell
proliferation, altered hypoxia sensing, and increased oxidative
stress^[56]2–[57]4. Cultured primary PAEC are a valid model system to
investigate the pathophysiology of PAH even after multiple passages ex
vivo as they accurately reflect endothelial cells in vascular lesions
of PAH lungs in vivo^[58]2,[59]3,[60]10–[61]13.
Until recently, traditional approaches have been used to focus on a
single molecule and/or pathway to investigate PAH, similar to
investigations of all complex disease phenotypes. However, recent
advances in proteomic and phosphoproteomic methodologies and human
interactome networks offer an unbiased approach for the identification
and quantification of many proteins and pathways in disease
pathology^[62]14. Alterations in the proteomes of plasma, lung tissues,
and pulmonary arterial smooth muscle cells from PAH patients were found
to be associated with disease progression, poor survival, and clinical
risk^[63]15–[64]17. Metabolomics, the quantification of small
biochemicals in plasma and tissues, can also provide new insights into
the complex biochemical processes of PAH and reveal relative activities
of pathways. Metabolomic analyses revealed metabolite alterations in
PAEC and pulmonary arterial smooth muscle cells relevant to
dysregulated vascular metabolism and disease
pathogenesis^[65]18,[66]19. To expand understanding of pathologic
molecular mechanisms, we hypothesized that integrative analyses of
protein expression and phosphorylation levels in endothelial cells
would reveal pathways important to the origins of PAH. To test this, we
used LC-MS/MS approach to analyze protein expression and
phosphorylation levels in PAEC from PAH (n = 4) and controls (n = 5).
We identified dysregulated pathways in PAH with integrative
bioinformatics and human protein-protein interactome network analyses
and confirmed related molecules and pathways with nontargeted
metabolomics and targeted high-performance liquid chromatography (HPLC)
of metabolites in plasma from PAH (n = 30) and healthy controls
(n = 12).
Results and Discussion
Human pulmonary arterial endothelial cells
PAEC were derived from 15 individuals with PAH undergoing lung
transplantation [age 33 ± 4 years; race, 11 white, 1 Asian, and 3
unknown; gender, 5 men, 9 women, and 1 unknown] and from donor lungs
not used for transplantation from 14 individuals [age 43 ± 4 years;
race, 9 white and 5 unknown; gender, 4 men, 9 women, and 1 unknown].
PAH was clinically confirmed by right heart catheterization [pulmonary
arterial pressure (PAP) mm Hg, 62 ± 3; pulmonary vascular resistance
(PVR) Wood units, 10 ± 1] and by pathologic review of explanted lungs.
Whole proteomics of PAH and healthy controls
To identify protein expression and phosphorylation in PAH, primary PAEC
derived from PAH lungs (n = 4) and control lungs (n = 5) were analyzed
(Fig. [67]1). A total of 2,556 proteins were identified by global
LC-MS/MS analysis (Supplementary Table [68]S1). In total, 170 proteins
were significantly different between PAH and control PAEC (P < 0.05).
Among them, 80 proteins were upregulated in PAH, while 90 proteins were
downregulated in PAH (Figs. [69]1 and [70]2a, Supplementary
Table [71]S2). Consistent with the high purity of PAEC cultures,
proteins expressed in smooth muscle or fibroblasts [SM22α, myosin heavy
chain and fibroblast-specific protein 1 (FSP1)] were undetectable in
PAH or control endothelial cells. To confirm discovery findings, 22
proteins among 170 differentially expressed proteins along with actin
and tubulin were picked to perform targeted analyses in 5 PAH PAEC from
5 different patients compared with control cells. Expression of actin
(ACTG1) or tubulin (TUBB) was similar between PAH PAEC (n = 5) and
controls (n = 5) (ACTG1, P = 0.8; TUBB, P = 0.8; two-tailed t-test).
Twelve of 22 proteins (55%) had the same direction of differential
expression between global and targeted proteomics. Within targeted
analyses, 8 of these 22 proteins (36.4%) were significantly
differentially expressed (P < 0.05, one-tailed) (Supplementary
Table [72]S2).
Figure 1.
Figure 1
[73]Open in a new tab
Workflow diagram summarizing proteomic and phosphoproteomic results in
pulmonary arterial endothelial cells (PAEC) from 4 pulmonary arterial
hypertension (PAH) patients and 5 control lungs. Only 4 differentially
expressed proteins were also differentially expressed as
phosphoproteins, and none of them were mitochondrial.
Figure 2.
[74]Figure 2
[75]Open in a new tab
Differentially expressed proteins and phosphoproteins related to
biological pathways and signaling networks in PAH PAEC. (a) Heatmap
clustering differentially expressed proteins between PAH PAEC (PAH,
n = 4) and healthy control PAEC (Ctrl, n = 5). Expression levels in PAH
relative to control are represented on a continuous scale from blue
(lowest) to pink (highest). (b) Top canonical pathways predicted by
Ingenuity Pathway Analysis (IPA) in differentially expressed proteins
between PAH and controls. (c) Heatmap clustering differentially
expressed phosphopeptides between PAH PAEC (PAH, n = 4) and healthy
control PAEC (Ctrl, n = 5). Expression levels in PAH relative to
control are represented on a continuous scale from blue (lowest) to
pink (highest). (d) Top canonical pathways predicted by IPA in
differentially expressed phosphoproteins between PAH and control PAEC.
Differentially expressed proteins were analyzed using Ingenuity Pathway
Analysis (IPA, QIAGEN) in which proteins are analyzed as a network
using canonical pathways, predicted upstream regulators, biological
functions and disease and functional networks. IPA analysis revealed
significant differences in 37 canonical pathways, 11 upstream
regulators, 22 biological functions, and 7 disease and functional
networks between PAH and control PAEC (Supplementary
Tables [76]S3–[77]S6). Canonical pathways predicted by IPA included
eukaryotic initiation factor (EIF) 2 signaling, NO and NOS3 signaling,
apelin, and hypoxia signaling (Fig. [78]2b, Supplementary
Table [79]S3), all of which have been previously
identified^[80]2,[81]5,[82]10,[83]12,[84]20–[85]22. IPA upstream
regulator analysis showed that cell division cycle 73 (CDC73), microRNA
223 (mir-223), endothelial PAS domain protein 1 (EPAS1, HIF-2α),
complement C1q binding protein (C1QBP), epidermal growth factor
receptor (EGFR), Wilms tumor-1 (WT1), and others were significantly
different in PAH PAEC (Supplementary Table [86]S4). Abnormal biological
functions such as cell death and survival (e.g., apoptosis, necrosis,
and cell death), cellular compromise and inflammatory response,
cellular movement, protein synthesis, and RNA post-transcriptional
modification were found to be significantly different in PAH PAEC
(Supplementary Table [87]S5) and confirmed previous findings by
traditional methods^[88]1–[89]5. Changes in seven disease and
functional networks (Supplementary Table [90]S6) including
hematological system development and function, and cardiovascular
system development and function [network 1], cellular compromise,
cellular function and maintenance, and cancer [network 2], cellular
movement, cell-to-cell signaling and interaction, cell cycle,
organismal injury and abnormalities, tissue morphology, cell death and
survival, and cellular development were associated with PAH.
Eukaryotic initiation factor (EIF) 2 is required for protein synthesis
and initiator binding of tRNA to the ribosome and plays a critical role
in vascular remodeling and proliferation of pulmonary arterial vascular
smooth muscle cells in hypoxia-induced pulmonary
hypertension^[91]20,[92]23,[93]24. Here, EIF2 signaling-related
proteins, including EIF2A, EIF3C, EIF4B, EIF4G3, EIF5B, 78 kDa
glucose-regulated protein (HSPA5), and 60S ribosomal protein L3 (RPL3),
were significantly upregulated, while RAC-alpha
serine/threonine-protein kinase (AKT1) and mitogen-activated protein
kinase 1 (MAPK1) were significantly downregulated in PAH (Supplementary
Tables [94]S2 and [95]S3). As predicted by IPA analysis, EIF2
signaling-related proteins participate in biological functions of
apoptosis, necrosis, cell death and protein synthesis (Supplementary
Table [96]S5) and are involved in the networks of cellular compromise,
cellular function and maintenance, and cancer (Supplementary
Table [97]S6).
Apelin is the endogenous ligand for the G-protein-coupled apelin
receptor that is expressed at the surface of the endothelium. Apelin
causes NO-dependent arterial vasodilation through NOS3 activation at
transcriptional and translational levels. Here, all apelin
signaling-related proteins including AKT1, calmodulin-3 (CALM3),
guanine nucleotide-binding protein subunit alpha-11 (GNA11), MAPK1,
microsomal glutathione S-transferase 1 (MGST1), NOS3, PRKACA, and SOD1,
were significantly reduced in PAH PAEC when compared to control PAEC
(Supplementary Table [98]S2). These effects might cause phosphorylation
inactivation of NOS3 and less NO^[99]10,[100]22. The findings confirm
prior work of Chun et al. on this pathway in PAH^[101]12. Other
apelin-mediated signaling pathways related to adipocytes, endothelial
cells, muscles, and cardiomyocytes were also significantly
downregulated in PAH PAEC as compared to control PAEC (Supplementary
Table [102]S3). Downregulated apelin signaling pathway are likely to
affect β-adrenergic receptor signaling and lead to dysregulated
vascular homeostasis and cardiovascular function in
PAH^[103]13,[104]25–[105]27.
Phosphoproteome of PAH and control PAEC
The characterization of the complex regulatory circuits underlying cell
response to external and internal stimuli is still limited by our
inability to describe the phosphorylation network on a global scale
although protein phosphorylation is known to modulate a wide variety of
processes. A total of 3,609 phosphopeptides derived from 1,411
phosphoproteins were identified by LC-MS/MS using phosphoserine and
phosphothreonine enrichment approach (Supplementary Table [106]S7). In
total, 240 phosphopeptides derived from 202 phosphoproteins were
significantly different between PAH and control PAEC (P < 0.05)
(Supplementary Table [107]S8). Among them, 128 phosphopeptides were
upregulated, while 112 phosphopeptides were downregulated in PAH PAEC
(Figs. [108]1 and [109]2c, Supplementary Table [110]S8). Most of these
have not been previously investigated in PAH.
Differentially expressed phosphoproteins were analyzed using IPA
(Supplementary Tables [111]S9–[112]S12). Eleven canonical pathways were
predicted by IPA with the top pathways being signaling by Rho family
GTPases, ILK signaling, RhoA signaling, and C-X-C-motif Chemokine
Receptor-4 (CXCR4) signaling (Fig. [113]2d, Supplementary
Table [114]S9). IPA analysis also predicted seven master regulators
with estrogen receptor-beta (ESR2) as the top regulator (Supplementary
Table [115]S10) and showed that PAH PAEC had abnormal biological
functions in the categories of cancer and organismal injury and
abnormalities (Supplementary Table [116]S11). Furthermore, the
differentially expressed phosphoproteins in PAH PAEC were associated
with nine top disease and functional networks (Supplementary
Table [117]S12), including networks of RNA post-transcriptional
modification, cellular development, cellular growth and proliferation
[network 1], cancer, organismal injury and abnormalities, endocrine
system disorders [network 2], gene expression, cell morphology, cell
death and survival, cellular assembly and organization, cellular
function and maintenance, organ morphology, organismal development,
hematological disease, immunological disease, hematological system
development and function, lymphoid tissue structure and development,
and infectious diseases.
Of the 170 differentially expressed proteins and the 240 differentially
expressed phosphopeptides, only 4 proteins were both differentially
expressed and had significantly different phosphorylation, i.e.,
eukaryotic translation initiation factor 5B (EIF5B), trans-Golgi
network integral membrane protein 2 (TGOLN2), polyadenylate-binding
nuclear protein 1 (PABPN1), and zinc finger CCCH domain-containing
protein 4 (ZC3H4) (Fig. [118]1, Supplementary Tables [119]S2 and
[120]S8).
EIF5B is responsible for catalyzing the formation of the ribosomal
initiation complex for translation. Phosphorylation of EIF5B enhances
ribosomal RNA processing^[121]28. Upregulated EIF5B protein expression
but downregulated EIF5B phosphorylation may have effects on RNA
translation in PAH PAEC (Proteomics, PAH/Control, fold-change
[FC] = 1.3075, P = 0.01; Phosphoproteomics, PAH/Control, FC = 0.3214,
P = 0.001).
TGOLN2 is a membrane protein localized to the trans-Golgi network that
plays a role in exocytic vesicle formation. TGOLN2 protein levels were
higher in PAH PAEC but were less phosphorylated (Proteomics,
PAH/Control, FC = 1.4642, P = 0.04; Phosphoproteomics, PAH/Control,
FC = 0.5661, P = 0.008). The effect of phosphorylation on TGOLN2 is
unknown. The differential expression of TGOLN2 in PAH PAEC suggests
that the sorting and secreting of proteins in the Golgi is altered in
PAH.
PABPN1 binds to poly A tails of nascent RNA and stimulates
polyadenylation, which increases message stability and decreases
alternative cleavage^[122]29. PABPN1 protein levels were significantly
lower in PAH than control PAEC but were more phosphorylated
(Proteomics, PAH/Control, FC = 0.6922, P = 0.04; Phosphoproteomics,
PAH/Control, FC = 1.5112, P = 0.02). Because phosphorylated PABPN1 is
implicated in double-strand break repair, the findings suggest
potentially greater ongoing DNA damage/repair in PAH PAEC^[123]29.
ZC3H4 is a zinc finger protein that is implicated in inflammation and
epithelial-to-mesenchymal transition in fibrosis^[124]30. PAH PAEC had
higher levels of ZC3H4 protein and greater protein phosphorylation than
control PAEC (Proteomics, PAH/Control, FC = 1.5377, P = 0.0003;
Phosphoproteomics, PAH/Control, FC = 1.5372, P = 0.03). This suggests
that PAH PAEC may have greater capacity to contribute to
fibrogenesis^[125]30.
Mitochondrial proteomes and phosphoproteomes
Previously we and others have shown that glucose metabolism and
mitochondrial respiration are altered in PAH patients and PAH
PAEC^[126]1,[127]2,[128]4,[129]5,[130]31. Mitochondria have their own
circular DNA (mtDNA), which contains 13 genes that encode proteins
essential for oxidative phosphorylation. However, most mitochondrial
proteins are encoded by nuclear DNA (MT-nDNA) and are imported to the
mitochondria^[131]32. We identified 670 mitochondrial proteins
(Supplementary Table [132]S13) with 45 proteins significantly different
between PAH and controls PAEC (P < 0.05). Among them, 23 were
upregulated while 22 were downregulated in PAH PAEC when compared to
control PAEC (Fig. [133]1, Supplementary Table [134]S14).
Phosphoproteomics identified 366 phosphopeptides derived from 154
mitochondrial proteins (Supplementary Table [135]S15). Eighteen
phosphopeptides derived from 18 mitochondrial proteins were
significantly different between PAH and control PAEC (P < 0.05). Among
them, 7 were upregulated in PAH while 11 were downregulated
(Fig. [136]1, Supplementary Table [137]S16). To our knowledge, nearly
half of them have not been previously reported in PAH. The search tool
for the retrieval of interacting genes/proteins (STRING) identified
these proteins in biological processes including organonitrogen
compound processes, metabolic processes of organic substance, small
molecule, cellular and primary, cellular component organization, and
response to stress identified in the mitochondrial proteome and
phosphoproteome are listed in Supplementary Tables [138]S17 and
[139]S18, respectively. Proteins related to organonitrogen compound
biosynthetic process, which maintains a state or activity of a cell,
were significantly altered in PAH, e.g.,
delta-1-pyrroline-5-carboxylate synthase (ALDH18A1), Golgi
phosphoprotein 3 (GOLPH3), mitochondrial 28S ribosomal protein (MRPS)
28, MRPS31, MRPS7, mitochondrial monofunctional C1-tetrahydrofolate
synthase (MTHFD1L), nucleoside diphosphate kinase A (NME1), RPL3,
mitochondrial serine hydroxymethyltransferase (SHMT2), stomatin-like
protein 2, mitochondrial (STOML2), and mitochondrial tyrosine–tRNA
ligase (YARS2) were significantly upregulated, and AKT1, microsomal
glutathione S-transferase 1 (MGST1), mannosyl-oligosaccharide
glucosidase (MOGS), tricarboxylate transport protein, mitochondrial
(SLC25A1), and v-type proton ATPase 116 kDa subunit a isoform 3
(TCIRG1) were significantly downregulated in PAH (Supplementary
Table [140]S14 and S17). Small molecule metabolic process involving low
molecular weight, monomeric and non-encoded molecules was changed in
PAH. Acetyl-CoA acetyltransferase (ACAT2), ALDH18A1, mitochondrial
enoyl-CoA delta isomerase 1 (ECI1), MTHFD1L, NADH dehydrogenase
[ubiquinone] 1 beta subcomplex subunit 7 (NDUFB7), NME1, SHMT2, STOML2
and YARS2 were significantly upregulated, while AKT1,
CDP-diacylglycerol–inositol 3-phosphatidyltransferase (CDIPT)
3-beta-hydroxysteroid-Delta(8),Delta(7)-isomerase (EBP),
isopentenyl-diphosphate Delta-isomerase 1 (IDI1), MAPK1 NOS3, SLC25A1,
and TCIRG1 were significantly downregulated (Supplementary
Table [141]S14 and S17), suggesting metabolic pathways and processes
involving small molecules were different in PAH as compared to healthy
controls. Furthermore, stress-related proteins had significantly
increased phosphorylation in transcription factor AP-1 (JUN) and
vimentin (VIM), but decreased phosphorylation in annexin A1 (ANXA1),
autophagy-related protein 16-1 (ATG16L1), inositol 1,4,5-trisphosphate
receptor type 3 (ITPR3), serine/threonine-protein kinase mTOR (MTOR),
myosin-10 (MYH10), poly [ADP-ribose] polymerase 4 (PARP4),
NAD-dependent protein deacetylase sirtuin-1 (SIRT1), DNA topoisomerase
2-alpha (TOP2A), and tumor suppressor p53-binding protein 1 (TP53BP1)
(Supplementary Table [142]S16 and S18), indicating that cellular
response to stress was significantly different in PAH endothelial
cells.
Human protein-protein interactome network analyses
Human interactome network analyses play crucial roles in drug target
discovery and in identifying pathobiological pathways in multiple
complex diseases^[143]33,[144]34. To build a comprehensive human
protein-protein interactome, we assembled data from 18 bioinformatics
and systems biology databases with multiple experimental pieces of
evidence (see Methods). We focused on experimentally validated
protein-protein interactions (PPIs), and the resulting human
interactome includes 351,444 PPIs connecting 17,706 unique
proteins^[145]33,[146]34. Subnetworks illustrated the full PPIs,
highlighting the PAH disease module formed by differentially expressed
proteins (P < 0.05, permutation test) and proteins with differentially
phosphorylated sites (P < 0.05, permutation test) (Fig. [147]3a). The
interactome network analyses show that differentially expressed
proteins have significant network proximity (see Methods) with
differentially expressed phosphorylated proteins in the human
interactome network (P < 0.0001, permutation test, Fig. [148]3b),
despite non-significant gene/protein overlap (P > 0.05, Fisher’s exact
test). In addition, mitochondrial differentially expressed proteins had
significant network proximity with mitochondrial differentially
expressed phosphorylated proteins in the human interactome as well
(P < 0.0001, permutation test, Fig. [149]3c). However, traditional
protein-overlap analysis shows no overlap between mitochondrial
differentially expressed proteins and mitochondrial differentially
expressed phosphorylated proteins (P > 0.05, Fisher’s exact test,
Fig. [150]1). For example, PPIs from three-dimensional (3D) protein
structures illustrated interactions between mitochondrial protein
calpain-1 catalytic subunit (CAPN1) and mitochondrial phosphorylated
protein VIM, and between mitochondrial protein HSPA5 and mitochondrial
phosphorylated protein TP53BP1. Furthermore, binary PPIs tested by
high-throughput yeast-two-hybrid (Y2H) systems illustrated interactions
between mitochondrial protein MAPK1 and mitochondrial phosphorylated
protein TOP2A, and among mitochondrial protein AKT1 and mitochondrial
phosphorylated proteins VIM and NAD-dependent protein deacetylase SIRT1
(Fig. [151]3a). Thus, human interactome network analyses highlight the
power to investigate pathobiological pathways related to PAH as
compared to traditional bioinformatics analysis (gene/protein overlap).
Figure 3.
[152]Figure 3
[153]Open in a new tab
An integrative human interactome network analysis in PAH. (a) The human
interactome network analysis shows that differentially expressed
proteins and phosphorylated proteins share many neighbors among
mitochondrial proteins and mitochondrial phosphorylated proteins
despite little overlap (Fig. [154]1). Protein-protein interaction (PPI)
lines are labeled by types of experimental evidence (color key of lines
at the left of figure) and serve as basis for constructing the network
(see Methods). 3D: three-dimensional and Y2H: Yeast Two-Hybrid.
Color-code circles at the left identify whether data is from proteome
[Non-mito. DE protein], mitochondrial proteome [Mito. DE protein],
phosphoproteome [Non-mito. protein with DE + P], or mitochondrial
phosphoproteome [Mito. protein with DE + P]. Node size is proportional
to p-value of differential expression analysis. Names of proteins are
in Supplementary Table [155]S1, phosphorylated proteins in
Supplementary Table [156]S7, mitochondrial proteins in Supplementary
Table [157]S13, and mitochondrial phosphorylated proteins in
Supplementary Table [158]S15. (b) Differentially expressed proteins had
significant network proximity with differentially expressed
phosphorylated proteins in the human interactome network (orange
arrow), although they did not have significant overlap at the protein
level. Blue bars indicate the proximity distribution from a permutation
test repeated 10,000 times using randomly selected proteins that
preserve the degree distribution. Network proximity was calculated
using the “Shortest” method. (c) Mitochondrial differentially expressed
proteins had significant network proximity with mitochondrial
differentially expressed phosphorylated proteins in the human
interactome (green arrow).
Differential metabolite analysis in PAH patients
To confirm that molecules and pathways associated with PAH in PAEC were
relevant in PAH in vivo, we performed non-targeted metabolomics and
targeted high-performance liquid chromatography of metabolites in
plasma from PAH patients (n = 30) and healthy controls (n = 12) from a
cohort previously described^[159]27,[160]35,[161]36 (Supplementary
Table [162]S19). Using non-targeted metabolomics analysis, we
identified 1,583 distinct named metabolites and 374 unnamed metabolites
in the plasma samples (Supplementary Table [163]S20). A total of 339
metabolites were significantly (P < 0.05) altered in plasma from PAH
patients. Of these, 198 metabolites were higher and 141 were lower in
PAH as compared with healthy controls. In addition, changes in 170
metabolites (113 up, 57 down) approached significance (0.05 < P < 0.10)
(Supplementary Table [164]S20). Pathway enrichment analysis revealed
significant changes in 17 biochemical pathways (P < 0.05, fold change >
1) relative to the overall change in PAH subjects, which included
aspartate and asparagine metabolism, purine salvage, urea cycle,
nicotinate metabolism and sphingolipid metabolism (Fig. [165]4a).
Similar to previously reported metabolic abnormalities described in
PAH^[166]18,[167]19, we found significantly increased levels of
glutamate, isocitrate, cis-aconitate, and purine metabolites including
fMet, guanosine, adenosine, inosine, xanthosine, and hypoxanthine.
Metabolomic analyses also showed significantly reduced glycine,
arginine, and citrulline levels in PAH (all P < 0.05) (Fig. [168]4b,
Supplementary Figs. [169]S1 and [170]S2, Supplementary Table [171]S20).
A total of 631 metabolites (excluding lipid metabolites) were utilized
for t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis. t-SNE
analysis showed clear separation of the PAH group from healthy controls
(Fig. [172]4c). We next sought to perform functional and integrative
analysis to further understand the mechanism underlying metabolic
abnormalities.
Figure 4.
[173]Figure 4
[174]Open in a new tab
Nontargeted metabolomics analysis in PAH. (a) Pathway enrichment
analysis revealed significant changes in differential metabolites
between PAH and controls (false discovery rate [FDR]). (b) Relative
abundances (Beanplots) of arginine, glutamate, symmetric
dimethylarginine (SDMA) + asymmetric dimethylarginine (ADMA),
spermidine, citrulline, nicotinamide, isocitrate, and cis-aconitate in
plasma were significantly different between PAH (n = 30) and healthy
controls (n = 12). Beanplots were prepared using R 3.5.1. (c)
t-Distributed Stochastic Neighbor Embedding (t-SNE) plot showed clear
separation of PAH group from healthy controls. t-SNE plot (2D
projections) was generated based on normalized metabolomics profiles
from PAH and healthy controls using the package Rtsne (version R
3.5.1).
Integrative analysis of mitochondrial proteomics and metabolomics
To comprehensively assess metabolic changes in PAH cells as compared to
control cells, we built an integrative metabolite-enzyme network
analysis (see Methods) by assembling data from three commonly used
metabolism databases: Kyoto Encyclopedia of Genes and Genomes
(KEGG)^[175]37, Recon3D^[176]38, and human metabolic atlas^[177]39. Via
an integrative network analysis of the metabolite-enzyme network and
differentially expressed proteins derived from mitochondrial proteomics
in PAEC, we identified several dysregulated pathways including SHMT2,
MTHFD1L, NOS3, AKT1, SLC25A1, ALDH18A1, and superoxide dismutase
[Cu-Zn] (SOD1) (Fig. [178]5a, Supplementary Fig. [179]S3a). Key
pathways identified through proteomic and metabolomic analyses are
described in greater details with their relevance to PAH. To assess
mitochondrial function in PAH PAEC, oxygen consumption rate (OCR) and
extracellular acidification rate (ECAR) were measured using a Seahorse
XF24 analyzer. We found that basal respiration in PAH PAEC (n = 3) was
significantly lower than in control PAEC (n = 5) (basal OCR pmol
O[2]/min, Controls 72 ± 4, PAH 44 ± 14, P = 0.03, Wilcoxon test). PAH
PAEC had less oxygen consumption than control PAEC for any glucose dose
provided to cells. As compared to control PAEC (n = 9), the curve of
PAH PAEC (n = 10) was significantly shifted to the left (P = 0.04)
(Fig. [180]5b). These results indicate functional biologic validation
of changes in metabolic pathways of PAH PAEC compared to control cells,
with PAH cells preferring glycolytic pathways and less oxidative
metabolism, i.e., the Warburg phenomenon.
Figure 5.
[181]Figure 5
[182]Open in a new tab
Dysregulated biological pathways and mitochondrial respiration in PAH.
(a) Integrative analysis of proteomics and metabolomics reveals
metabolic changes in PAH. Red arrow denotes elevated in PAH, and green
arrow denotes decreased in PAH. Differentially expressed mitochondrial
proteins by proteomics framed with black. Blue star, NAD^+ is reduced
to NADH. ADMA, asymmetric dimethylarginine; αKG, alpha-ketoglutarate;
AKT1, RAC-alpha serine/threonine-protein kinase; ALDH18A1,
delta-1-pyrroline-5-carboxylate synthase; ARG2, arginase 2; ETC,
electron transport chain; fMet, N-formylmethionine; MTHFD1L,
monofunctional C1-tetrahydrofolate (THF) synthase; NO, nitric oxide;
NOS3, endothelial nitric oxide synthase; P5C,
Δ1-pyrroline-5-carboxylate; SHMT2, serine hydroxymethyltransferase;
SLC25A1, tricarboxylate transport protein; SOD1, superoxide dismutase
[Cu-Zn]; TCA cycle, tricarboxylic acid cycle, THF, tetrahydrofolate.
(b) Extracellular acidification rate (ECAR) vs. oxygen consumption rate
(OCR) in PAH PAEC and healthy control PAEC. Mean of measurements are
shown following addition of glucose at specified doses. PAH PAEC
(n = 10) had a significant shift of the curve compared to control PAEC
(n = 9) (P = 0.04).
Reduced antioxidant response
SOD1, which binds copper and zinc ions, is a ubiquitous enzyme with an
essential function in protecting aerobic cells against oxidative stress
and is mainly expressed in the matrix of the mitochondria. SOD1 acts as
a homodimer to convert superoxide to oxygen and hydrogen peroxide.
Previously we and others identified increased oxidative stress and
decreased SOD activity in PAH lungs and
PAEC^[183]1,[184]5,[185]40–[186]42. Here, significantly reduced
expression of SOD1 detected by proteomics (P = 0.04) (Fig. [187]5a,
Supplementary Table [188]S14) was confirmed by Western blot analyses
(Controls 1 ± 0.1, n = 7, PAH 0.7 ± 0.1, n = 7, P = 0.01, one-tailed
t-test) (Supplementary Fig. [189]S3b). To evaluate oxidative stress in
PAH PAEC, menadione, which undergoes redox cycling to produce ROX via
NADPH cytochrome P450 reductase and mitochondrial complex I
(NADH-Ubiquinone oxidoreductase), was used to assess the capacity of
PAEC to produce ROS. Baseline unstimulated ROS in quiescent control
(n = 5) and PAH PAEC (n = 4) were similar (CellROX median fluorescence
intensity Controls, 2453 ± 541; PAH 2051 ± 731, P = 0.6), but menadione
led to a subset of cells in both PAH and controls that exhibited high
CellROX staining (% CellROX^hi, Controls 29 ± 13; PAH 28 ± 14,
P = 0.9). Within the CellROX^hi subset, the ROS level generated by PAH
PAEC exposed to menadione was significantly higher than in control
cells (CellROX Median Fluorescence Intensity menadione/baseline,
Controls 0.90 ± 0.05; PAH 1.06 ± 0.06, P = 0.04; one-tailed t-test),
indicating greater capacity for ROS generation in PAH PAEC under
conditions of redox stress. In addition, mitochondrial protein MGST1,
which protects against oxidative stress and regulates mitochondrial
metabolism^[190]43, was downregulated in PAH (P = 0.01) (Supplementary
Tables [191]S14 and [192]S17). Glutathione peroxidases (GPX) are
additional antioxidants that play a major role in the protection
against oxidative stress by detoxification of hydrogen peroxide.
Network analysis showed that two isoforms of GPX were lower in PAH
(GPX1, P = 0.06; GPX4, P = 0.11) (Supplementary Table [193]S1), and
STRING analysis reveals that they are partners of SOD1. Taken together,
network analysis offers potential evidence that increased oxidative
stress in PAH is due to loss of antioxidant response (Fig. [194]5a).
Accelerated one carbon metabolism
One carbon metabolism in mitochondria is essential for the biosynthesis
required for cell proliferation and pivotal for redox balance during
hypoxia^[195]44,[196]45. Proteomic studies revealed two key enzymes in
one carbon pathway [SHMT2 (P = 0.036) and MTHFD1L (P = 0.04)], both of
which affect organonitrogen compound biosynthetic and metabolic process
pathways, were significantly elevated in PAH (Supplementary
Fig. [197]S3a, Supplementary Tables [198]S14 and [199]S17).
Metabolomics data were consistent with proteomics data (Fig. [200]4,
Supplementary Fig. [201]S1). SHMT2 is a mitochondrial protein that
catalyzes serine and glycine to tetrahydrofolate (THF) and
5,10-methylene-THF. Interestingly, global metabolomics of PAH revealed
lower plasma glycine levels (Supplementary Fig. [202]S1) supporting
higher enzyme activity of SHMT2. MTHFD1L catalyzes 10-formyl-THF, an
important precursor in purine and N-formylmethionine (fMet) synthesis,
to formate. Global plasma metabolomics identified an increase in purine
metabolites in PAH, including fMet (P = 0.02), guanosine (P = 0.001),
adenosine (P = 0.0001), inosine (P = 0.005), xanthosine (P = 0.04), and
hypoxanthine (P = 0.002) (Supplementary Fig. [203]S1), supporting the
hyperproliferative phenotype of PAH PAEC as previously
reported^[204]1,[205]3. Integrative network analyses of proteomics and
metabolomics indicate that one carbon pathway is dysregulated in PAH
(Fig. [206]5a).
Downregulated nitric oxide and arginine pathways
The hallmark of endothelial vascular dysfunction in PAH is the impaired
production of NO by NOS3. Underlying mechanisms include loss of NOS3,
inactivation of NOS3, and/or decreased NOS3 substrate arginine
bioavailability due to increased
arginases^[207]2,[208]4,[209]10,[210]46. Here, NOS3 protein and AKT1,
which positively regulates NOS activity via phosphorylation of
NOS3^[211]47, were significantly lower in PAH PAEC (Supplementary
Fig. [212]S3a, Supplementary Table [213]S14). Other proteins in the
arginine/NO pathway were also differentially expressed, such as
NOS-interacting protein (NOSIP), cAMP-dependent protein kinase
catalytic subunit alpha (PRKACA), and CALM3 (Supplementary
Tables [214]2 and [215]3). The plasma metabolome of PAH also shows that
endogenous NOS inhibitors dimethylarginines (DMA) (symmetric DMA +
asymmetric DMA) and monomethylarginine (MMA) were significantly
increased in PAH (Fig. [216]4b, Supplementary Fig. [217]S2).
Furthermore, arginine and citrulline levels were significantly lower in
plasma of PAH patients (Fig. [218]4b, Supplementary Fig. [219]S2).
Arginase, a critical enzyme in the urea cycle, converts arginine to
ornithine and urea and plays a regulatory role in NO synthesis by
modulating the availability of arginine for NOS^[220]48. Because
arginases are intracellular enzymes that appear in the circulation only
after cell damage or death, arginine-to-ornithine ratio has been
suggested as a better assessment of total-body arginase
activity^[221]49. The arginine-to-ornithine ratio was lower in plasma
from PAH patients compared with controls (P = 0.04) (Supplementary
Fig. [222]S2), indicating substrate limitation for NOS in PAH in
vivo^[223]2,[224]46. Collectively, the proteome and metabolome of PAH
confirms that the NOS3 pathway is impaired (Fig. [225]5a).
Abnormal tricarboxylic acid (TCA) cycle flux
TCA cycle has a primary role in oxidation of substrate for energy
production (ATP) but also serves critical biosynthetic functions in
which intermediates enter and leave the cycle to regulate cell
metabolism and signal transduction^[226]50. SLC25A1, a mitochondrial
citrate carrier, exports citrate from mitochondria to cytoplasm
(Supplementary Table [227]S17)^[228]51. SLC25A1 mutations that
inactivate the citrate export pathway lead to severe mitochondrial
dysfunction^[229]51. Proteomic study of PAEC showed lower levels of
SLC25A1 in PAH (P = 0.03) (Supplementary Fig. [230]S3a, Supplementary
Table [231]S14) that were confirmed by Western blot analyses (Controls
1 ± 0.1, n = 5, PAH 0.4 ± 0.1, n = 4, P = 0.01, one-tailed t-test)
(Supplementary Fig. [232]S3b), suggesting that the concentration of
mitochondrial citrate may be higher than cytosolic citrate. The plasma
metabolome showed higher isocitrate (P = 0.009) and cis-aconitate
(P = 0.014) in PAH (Fig. [233]4b). In addition, citrate was previously
reported to be increased in PAH lungs^[234]21. The findings suggest
that decreased SLC25A1 protein influences the TCA cycle flux in PAH
(Fig. [235]5a).
Alteration of glutamate metabolism in PAH
ALDH18A1 catalyzes glutamate to Δ1-pyrroline-5-carboxylate (P5C), a
major step in the biosynthesis of proline, ornithine, and
arginine^[236]52,[237]53. Proteomics showed higher levels of ALDH18A1
(P = 0.02) (Supplementary Fig. [238]S3a, Supplementary Table [239]S14).
Consistent with our result, others have found increased expression of
ALDH18A1 in PAH lungs^[240]21. High levels of plasma glutamate
(P = 0.003), a substrate of ALDH18A1, and spermidine (P = 0.002),
downstream of ornithine, were found in PAH (Fig. [241]4b). The
upregulated ALDH18A1 and consequent elevated spermidine in PAH
(Figs. [242]4b and [243]5a) is similar to findings in rapidly growing
malignant cells as predicted by IPA (Supplementary Table [244]S6).
Abnormal mitochondrial phosphoproteins related to fatty acid metabolic
pathway, cristae morphology, and deacetylase activity of PAH PAEC
Acetyl-CoA carboxylase 1 (ACACA), mitochondrial contact site and
cristae organizing system (MICOS) complex subunit MIC19 (CHCHD3), and
SIRT1 were some of the differentially expressed mitochondrial
phosphoproteins (Supplementary Table [245]S16).
An imbalance between glycolysis, glucose oxidation, and fatty acid
oxidation has been reported in PAH^[246]21,[247]54. Mitochondrial
ACACA, a downstream target of AMP-activated protein kinase (AMPK),
catalyzes a rate-limiting reaction in the biogenesis of long-chain
fatty acids. Recent reports show that knockdown of ACACA by siRNA
limits fatty acid supply to TCA cycle and induces apoptosis in cancer
cells. Phosphorylation of ACACA via AMPK leads to inactivation and
changes in metabolism of cancer cells^[248]55,[249]56. Thus, the low
phosphorylation state of ACACA in PAH (P = 0.001)(Supplementary
Table [250]S16) may enhance catalytic function and increase fatty acid
metabolism in PAH^[251]57. Consistent with lower phosphorylated ACACA,
other proteins involved in fatty acid metabolism, e.g., ACAT2 and ECI1,
were also differentially expressed in PAH. ACAT2 protein levels were
significantly decreased in PAH (P = 0.001) (Supplementary
Table [252]S14), while ECI1 protein levels were significantly increased
in PAH (P = 0.004) (Supplementary Table [253]S14), indicating abnormal
fatty acid metabolic pathways in PAH.
CHCHD3, an inner mitochondrial membrane protein, plays an important
role in the maintenance of the mitochondrial contact site and
cristae-organizing system (MICOS) complex stability and mitochondrial
cristae morphology^[254]58. Mitochondrial cristae are the site of
oxidative phosphorylation (OXPHOS) and modulators of mitochondrial
bioenergetics^[255]59. Alteration of phosphorylation of CHCHD3
(P = 0.007) could lead to reductions in oxygen consumption and ATP
production and affect cell growth, cell death, and survival
(Supplementary Tables [256]12 and [257]16)^[258]58. Two functional
partners of CHCHD3, mitochondrial-localized MICOS complex subunit MIC27
(APOOL) and coiled-coil-helix-coiled-coil-helix domain-containing
protein 2 (CHCHD2) predicted by STRING analyses, were identified by
proteomics (Supplementary Table [259]S13). Protein levels of CHCHD2
required for MICOS complex at the cristae junction tended to be
increased in PAH (P = 0.10) (Supplementary Table [260]S13)^[261]60.
Downregulated APOOL protein levels (P = 0.07) (Supplementary
Table [262]S13) would cause major alterations in cristae morphology and
impair mitochondrial respiration^[263]61.
Mitochondrial phosphor-SIRT1, one of the seven sirtuins linked to
longevity in mammals, had significantly reduced phosphorylation in PAH
(P = 0.04) (Supplementary Table [264]S16). Sirtuins mediate NAD+
-dependent deacetylation of targets such as AKT signaling and
peroxisome proliferator-activated receptor gamma coactivator 1-alpha
(PGC-1α) (Supplementary Tables [265]S10–[266]S12, and
[267]S18)^[268]62,[269]63. PPI between SIRT1 and AKT1 was identified by
high-throughput Y2H systems (Fig. [270]3a). Sirtuin signaling
pathway-related proteins, such as NDUFB7 (P = 0.0009), peptidyl-prolyl
cis-trans isomerase D (PPID) (P = 0.02), and X-ray repair
cross-complementing protein 5 (XRCC5) (P = 0.04) were significantly
upregulated in proteomic analysis (Supplementary Tables [271]2, [272]3
and [273]14). Metabolomics also showed that nicotinamide, as part of
the coenzyme nicotinamide adenine dinucleotide (NADH / NAD+), was
increased in PAH (P < 0.0001) (Figs. [274]4b and [275]5a). These
integrative network analyses support that abnormal phosphorylation of
SIRT1 might cause a decline in its deacetylase activity, leading to
metabolic redox perturbations in PAH (Supplementary
Table [276]S18)^[277]64.
Conclusion
In this study, 170 of 2,556 proteins and 240 of 3,609 phosphopeptides
were significantly different between PAH PAEC and control PAEC. Among
the proteins encoded by mtDNA or transcribed from nuclear DNA and
imported to the mitochondria, 670 mitochondrial proteins and 366
mitochondrial phosphopeptides were identified, including 45
mitochondrial differentially expressed proteins and 18 mitochondrial
differentially expressed phosphopeptides. Many of these proteins were
previously unknown and unsuspected to be altered in PAH. An integrative
network analysis of multi-omics data in PAEC and plasma uncovered
dysregulated pathways particularly related to mitochondria, e.g.,
accelerated one carbon metabolism, abnormal TCA cycle flux and
glutamate metabolism, dysfunctional arginine and NO pathways, and
enhanced oxidative stress. Functional studies in PAH PAEC confirmed
decreases in mitochondrial oxygen consumption and increases in
oxidative stress. Overall, the complementary and synergistic
integrative analyses of proteomics and metabolomics provide an
unparalleled opportunity to achieve a broad understanding of mechanisms
underlying the disease and identify new targets for future
therapeutics.
Methods
Pulmonary arterial endothelial cell cultures
Human primary pulmonary arterial endothelial cells (PAEC) were
collected either from unused explanted control donor lungs or explanted
from PAH patients undergoing lung transplantation who provided informed
written consent or under an IRB exempt protocol. The study was approved
by the Institutional Review Board of the Cleveland Clinic. All methods
were performed in accordance with the relevant guidelines and
regulations.
PAEC from fifteen explanted PAH lungs or ten donor lungs not used for
transplantation were harvested and cultured as previously
described^[278]4,[279]10,[280]11. Four additional control PAEC were
purchased from Lonza (Walkersville, MD). Cells were passaged at 70–80%
confluency, and primary cultures of passages 6–7 used in experiments.
Purity of the cultured PAEC were evaluated with flow cytometry. The
purity of each PAEC was assessed with CD31; the mean purity of PAEC was
98.5% and individual cultures were all above 96.8% (Supplementary
Table [281]S21)^[282]11. There was no difference between control PAEC
commercially purchased or harvested at Cleveland Clinic; they were
identical in expression patterns for proteins (Supplementary
Fig. [283]S4, Supplementary Table [284]S21).
Mass spectrometry analysis
For global proteomics, a 50 μg aliquot of protein from each PAEC sample
was subjected to in-gel digestion in which the whole gel lane was
divided into 13 areas, and the gel pieces were washed/destained in 50%
ethanol, 5% acetic acid, dehydrated in acetonitrile, and then prepared
as previously described^[285]26,[286]65. MaxQuant V1.5.2.8 with the
search engine Andromeda integrated into MaxQuant software was used to
analyze the data, and default settings were used as the parameters for
the Orbitrap instrument. The database used to search the MS/MS spectra
was the Uniprot human protein database containing 85,299 protein
sequences. CID spectra collected in the experiment were used for
peptide sequencing and protein identification, and full scans were used
for peptide precursor intensity calculations. For targeted proteomics,
a parallel reaction monitoring (PRM) experiment was performed on a
Thermo Scientific Fusion Lumos instrument. The peptides targeted in
these analyses were chosen based on the presence in the initial data
dependent analyses, unique to the protein of interest, and the shape of
the chromatographic peaks^[287]66.
For global phosphoproteomic analysis, a 1.0 mg aliquot of protein from
each PAEC sample was subjected to serine and threonine phosphorylation
enrichment using Thermo Scientific™ Pierce™ TiO2 (Thermo Scientific™
Pierce™) and C18 clean-up (Fisher # PI88301) prior to LC-MS/MS
analysis. The LC-MS/MS system was a Thermo Fisher Scientific
LTQ-Obitrap Elite hybrid mass spectrometer. The data was acquired using
the data dependent method described above. MaxQuant V1.5.2.8 with the
search engine Andromeda integrated into MaxQuant software was used to
analyze the data, and default settings were used as the parameters for
the Orbitrap instrument with the addition of phosphorylation at S, T,
and Y residues as a variable modification. Phosphorylated peptides
containing one or more modification sties were considered. The site
localization threshold of greater than 50% was used and this resulted
in a majority of phosphorylation events occurring on Serine residues,
6,113 (84.3%), followed by Threonine, 1,065 (14.7%), with 74(1%)
occurring on Tyrosine residues.
Label‐free quantitation (LFQ) for both the proteomics and
phosphoproteomics by MaxQuant was used to determine intensity and
normalize protein quantities. LFQ intensities are the output of the
MaxLFQ algorithm^[288]14.
Quantitative analysis of proteome and phosphoproteome
Patient characteristics were summarized with appropriate descriptive
statistics. For the protein expression analysis, the
variance-stabilizing transformation (VSN) was first performed to
normalize the proteomics and phosphoproteomics data, respectively.
Missing data were imputed using the nearest neighbor approach. The
comparison between PAH and healthy controls was then performed using
the moderated t-test (limma package in R). The results were controlled
for multiple comparisons with the false discovery rate (FDR) approach,
and proteins with significantly different expressions were identified
with FDR < 0.05. All analyses were conducted using R-studio (Boston,
MA).
Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR)
OCR and ECAR were measured using the Seahorse Extracellular Flux (XF24)
Analyzer (Seahorse Bioscience Inc. North Billerica, MA) according to
manufacturer’s protocol. Seahorse assay media (DMEM without glucose,
l-glutamine, phenol red, sodium pyruvate, and sodium bicarbonate
[Sigma-Aldrich] supplemented with either 1.08 g/l glucose, 1.85 g/l
sodium chloride, 1 mM sodium pyruvate, and 15 mg/l phenol red
[MitoStress Assay] or 1.85 g/l sodium chloride and 3 mg/l phenol red
[glucose dose response assay]) was supplemented with 2 mM l-glutamine
and the pH adjusted to 7.35 with sodium hydroxide. PAEC were plated at
a density of 50,000 cells per well in MCDB107 growth media overnight,
with 3 wells per plate left empty for background correction. Growth
medium was removed, and cells were washed with the appropriate Seahorse
assay medium three times. After the final wash, assay medium was added
to each well at a final per-well volume of 500 μl. The plate was
incubated in a 37 °C non-CO[2] incubator for one hour. The plate was
then transferred to the Seahorse XF24 Analyzer for analysis. For the
basal oxygen measurement, OCR was measured in PAEC in assay medium
containing glucose, with 5 replicates per cell. For the glucose dose
response test, PAEC underwent basal measurement of extracellular
acidification in glucose-free assay medium, followed by addition of
glucose at 0 mM, 0.1 mM, 0.3 mM, 1 mM, 3 mM, 10 mM, 30 mM, or 100 mM,
with 2–3 wells per glucose concentration. All measures were done 3
times in a 3–2–3-minute mix-wait-measure cycle. The area under the
curve for OCR and ECAR was compared between PAH PAEC and control PAEC
using the bootstrap method.
Signaling pathway analyses of proteome and phosphoproteome
Canonical pathway analysis, upstream regulator analysis, and
interaction network analysis of differentially expressed proteins as
well as phosphoproteins were done with IPA (Qiagen, Redwood City). The
search tool for the retrieval of interacting genes/proteins (STRING)
([289]http://string-db.org) was used to predict functional partners and
biological pathways.
Construction of human protein-protein interactome
To build a comprehensive human interactome network, we integrated data
from a total of 18 differential bioinformatics databases with multiple
experimental evidences. Specifically, we used PPIs with six types of
experimental evidence: (i) binary PPIs tested by systematic,
high-throughput yeast-two-hybrid (Y2H) systems; (ii) binary PPIs from
three-dimensional (3D) protein structure data; (iii) kinase-substrate
interactions from literature-derived experimental data; (iv) protein
signaling network from literature-derived low-throughput experiments,
(v) protein complexes (approximately 56,000 PPIs) tested by an affinity
purification-mass spectrometry assay, and (vi) literature-curated PPIs
identified by affinity purification followed by mass spectrometry
(AP-MS), Y2H, and by literature-derived experimental data. The genes
have been mapped to Entrez ID and their official gene symbols based on
GeneCards ([290]http://www.genecards.org/). We removed all
computationally predicted data, such as evolutionary analysis, gene
co-expression network, and metabolic associations. In total, the
resulting human interactome included 351,444 PPIs connecting 17,706
unique proteins. More detailed descriptions are provided in our recent
studies^[291]33,[292]34.
Network proximity analysis
To inspect the network relationship between differentially expressed
proteins and differentially expressed phosphorylated proteins, we
performed network proximity analysis. Specifically, network proximity
quantifies the average shortest path length between two different
protein sets in the human interactome. Given A and B, the set of
proteins for A (e.g., differentially expressed proteins) and B (e.g.,
differentially phosphorylated proteins), and d[AB], the shortest path
length between nodes a and b, we define the network proximity measure
as follows:
[MATH: 〈dA
Bs〉=1‖A‖×
‖B‖∑a∈A,b∈Bd(a,b) :MATH]
1
To evaluate the significance of the network distance between two
protein sets, we built a reference distance distribution corresponding
to the expected distance between two randomly selected groups of
proteins with the same size and degree (connectivity) distribution as
the original protein set. We repeated this procedure 10,000 times. The
mean
[MATH: d¯ :MATH]
and standard deviation (σ[d]) were used to calculate a z-score (z[d])
by converting an observed (non-Euclidean) distance to a normalized
distance. We computed P-value by 10,000 permutation tests. More details
of network proximity analysis are given in our previous
studies^[293]33,[294]34.
Metabolomic analysis
Nontargeted metabolomic analysis was performed on plasma samples
collected from 30 PAH and 12 healthy controls (Supplementary
Table [295]S19) by Metabolon (Durham, NC) as previously
described^[296]67.
Metabolite-enzyme network analyses
We built a comprehensive metabolite-enzyme network by assembling data
from three commonly used metabolism databases: KEGG^[297]37,
Recon3D^[298]38, and human metabolic atlas^[299]39. We then mapped the
differentially expressed proteins (enzymes) and significant metabolites
into the metabolite-enzyme network to identify the dysregulated
metabolism pathways in PAH.
Supplementary information
[300]Supplementary Information^ (1.4MB, pdf)
[301]Supplementary Table S1^ (159.2KB, xlsx)
[302]Supplementary Table S7^ (280.6KB, xlsx)
[303]Supplementary Table S13^ (41.5KB, xlsx)
[304]Supplementary Table S15^ (37.6KB, xlsx)
[305]Supplementary Table S20^ (375.1KB, xlsx)
Acknowledgements