Serum proteins differentially expressed in early- and late-onset preeclampsia assessed using iTRAQ proteomics and bioinformatics analyses

View article
Bioinformatics and Genomics


Preeclampsia (PE), a hypertensive disorder complicating pregnancy, is the main cause of increased perinatal mortality among mothers and infants worldwide. The rate of PE in the United States is approximately 3.4% (Ghulmiyyah & Sibai, 2012), and it is higher in developing countries (Ananth, Keyes & Wapner, 2013). PE is marked by new-onset hypertension occurring after 20 weeks of gestation, accompanied by either new-onset proteinuria or systemic multiple organ damage (Committee on Practice Bulletins—Obstetrics, 2019). Severe PE may lead to convulsions, coma, cerebral hemorrhage, heart failure, placental abruption, disseminated intravascular coagulation, and even death (Bibbins-Domingo et al., 2017). Fetal delivery is the most effective treatment for PE, with other treatments of relieving symptoms used only as an attempt to gain time for enabling further maturity of the fetus. As a result, PE is the leading cause of premature birth and low birth weight, especially severe PE (Ananth & Vintzileos, 2006).

The exact pathogenesis of PE remains unclear. However, PE is considered a placenta-derived disease because the syndrome resolves once the placenta is removed. There are two subtypes of PE: early- and late-onset preeclampsia. Their pathogeneses are not identical. Early-onset preeclampsia (EOPE) has a higher degree of placental damage, whereas late-onset preeclampsia (LOPE) may focus on the interaction between the normal senescence of the placenta and a maternal genetic susceptibility to cardiovascular and metabolic disease (Burton et al., 2019). However, pathophysiological changes of the placenta further lead to endothelial dysfunction and systemic inflammatory response, which are their common pathogenesis link (Young, Levine & Karumanchi, 2010).

The placenta is in direct contact with the maternal circulation. Therefore, any changes in the protein expression of placental tissue are reflected in maternal serum proteins (Norwitz, 2007). If the types and levels of differentially expressed proteins (DEPs) in the serum of patients with PE can be researched quantitatively and holistically, specific serum biomarkers may be found for predicting PE and for further study of its pathogenesis. Compared with other proteomics methods, isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS) is considered more effective for searching for serum or plasma biomarkers (Moulder et al., 2018). Therefore, in the present study, we used iTRAQ and LC-MS/MS methods to identify serum proteins differentially expressed between EOPE and LOPE and between women with PE and healthy pregnant women. We then used bioinformatics analyses to determine the key proteins and related signaling pathways associated with the development of EOPE and LOPE.

Materials & Methods

Participants and clinical samples

The Medical Research Ethics Committee of Anhui Medical University reviewed and approved our research protocol and an informed patient consent form (Anhui Medical Ethics approval No. 20150192). All patients signed the approved written informed consent form prior to being included in the study.

Blood samples were collected from December 2018 to May 2019 from 16 pregnant women, eight of whom had received a diagnosis of PE, and were in the Maternal and Child Health Hospital Affiliated with Anhui Medical University. A PE diagnosis was made consistent with the 2019 American College of Obstetricians and Gynecologists pregnancy hypertension guidance (Committee on Practice Bulletins—Obstetrics, 2019). All included pregnant women were primipara and without a disease that may have affected their serum protein levels, such as infection, multiple pregnancy, or gestational diabetes mellitus. Four serum samples were obtained from and allocated to each of the following four groups: EOPE, the EOPE control, LOPE, and the LOPE control groups.

Protein extraction and quality testing

High-abundance proteins were removed from the serum samples using Pierce Top 12 Abundant Protein Depletion Spin Columns (Thermo Fisher). The protein concentration was determined by the Bradford method. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was performed to separate the proteins and evaluate the quality of samples.

Trypsin enzymatic hydrolysis and peptide iTRAQ isobaric labeling

After protein quantification, a centrifuge tube containing 60 µg of protein solution was mixed with 5 µL 1 M dithiothreitol at 37 °C for 1 h. Then 20 µL of 1 M iodoacetamide was added and the solution allowed to react for 1 h at room temperature. All samples were pipetted into ultrafiltration tubes, and the filtrate was discarded after centrifugation. UA buffer (8 M urea, 100 mM Tris-HCl, pH 8.0, 100 µL) was added and the sample centrifuged at 14,000 g for 10 min; this step was repeated twice. Then, 50 mM NH4HCO3 (50 µL) was added and the filtrate discarded after centrifugation; this step was repeated three times. Trypsin buffer (40 µL) was added and mixed, and the samples were centrifuged at 600 rpm for 1 min. The samples were then subjected to enzymatic hydrolysis at 37 °C for 12–18 h. Transfer the enzymatic hydrolysate to a new centrifuge tube. After labeling with 8plex iTRAQ reagents multiplex kit, the same volume of each sample was mixed together and desalted using a C18 cartridge.

LC-MS/MS analysis

The labeled samples were redissolved in 40 µL of 0.1% formic acid aqueous solution. The peptides were loaded onto a C18-reversed phase column (3 µm C18 resin, 75 µm ×15 cm). The mobile phases consisted of 2% methyl cyanide/0.1% formic acid/98% water and 80% methyl cyanide/0.08% formic acid/20% water. The gradient for the B phase increased linearly at 0–68 min from 7% to 36% and at 68–75 min, from 36% to 100%. Each sample was separated by capillary high-performance liquid chromatography and was analyzed by an Orbitrap Fusion Lumos mass spectrometer (Thermo Science). After the data were collected, they were processed according to the flowchart shown in Fig. 1. Protein identification and quantification were done by using the ProteinPilot Software, version 4.2 (Sciex). Human proteome databases containing UniProt sequences were used to perform peptide identification. Serum proteins with fold changes in their levels compared with control groups of at least 1.2 (in two of two replicates) and that were also statistically significantly different between the groups at P < 0.05 were further analyzed.

Flowchart of the data analysis procedure.

Figure 1: Flowchart of the data analysis procedure.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses

Metascape, a web-based resource ( for gene and protein annotation, visualization, and integration discovery (Fang et al., 2019; Soonthornvacharin et al., 2017) was used to perform GO analyses. The KOBAS online analysis database ( was used to performed KEGG pathway analyses (Kanehisa & Goto, 2000). A two-sided P < 0.05 was regarded significantly different.

Enzyme linked immunosorbent assay (ELISA)

PSG9, the downregulated protein in the LOPE group, was verified by ELISA kit (specification and catalogue number: 96T, OM626395). The experiment was performed according to the manufacturer instructions. Blood samples were collected from women with LOPE or normal pregnancies.

Statistical analysis

Two-tailed Mann–Whitney U test was performed with SigmaPlot software. Values are expressed as means ± SEM. A value of P < 0.05 was considered statistically significant.



The mean and standard deviation (SD) age of the patients in the EOPE group was 31.8 ± 5.0 years, and they were at a mean (SD) gestation of 31.9 ± 2.9 weeks; the mean (SD) age of the participants in the EOPE control group was 30.0 ± 1.0 years, with a mean (SD) gestation of 31.5 ± 1.4 weeks. No significant difference was found in age (P = 0.57) or gestational weeks (P = 0.84) between these two groups. The mean (SD) age of the patients in the LOPE group was 24.8 ± 1.5 years, with 37.8 ± 1.5 weeks’ gestation; the mean (SD) age of the participants in the LOPE control group was 27.0 ± 1.3 years, with a mean (SD) of 38.9 ± 1.3 weeks’ gestation. No significant difference was found in age (P = 0.15) or gestation (P = 0.37) between these two groups.


The total proteins in the molecular weight range of 10–220 kDa from 16 samples were effectively separated by SDS-PAGE. The proteins were not degraded, and the high-abundance proteins were obtained (Fig. 2).

Removal of high-abundance proteins.

Figure 2: Removal of high-abundance proteins.

(A) Lane E is the original sample from the early-onset preeclampsia (EOPE) group. Lane M contains markers. Lanes 1–4 are from serum samples obtained from the EOPE group after removing the highly abundant proteins, whereas lanes 5–8 are from the EOPE control group after removing the highly abundant proteins. (B) Lane L is the original sample from the late-onset preeclampsia (LOPE) group. Lane M contains markers. Lanes −1 to −4 are from the LOPE group after removing the highly abundant proteins, whereas lanes −5 to −8 are from the LOPE control group after removing the high-abundance proteins. The bands are clear and uniform and without protein degradation.

MS/MS spectrum analysis and Identification of DEPs

LC-MS/MS is a powerful tool for identifying proteins in serum samples. We identified 413 serum proteins in the EOPE and EOPE healthy control groups, of which 70 were significantly and differentially expressed between the two groups, with 51 upregulated and 19 downregulated (Table 1, Fig. 3). We also identified 470 proteins in the LOPE and LOPE control groups, of which 24 proteins were significantly and differentially expressed between the two groups, with 10 upregulated and 14 downregulated (Table 2, Fig. 3). Clustergrams generated describing the expression of these DEPs indicated that the expression patterns between the patient EOPE or LOPE groups obviously differed from their control groups, but the two patient groups, EOPE and LOPE, clustered together (Figs. 3B and 3D).

GO functional annotation and enrichment analysis

GO analysis is an important method and tool in the field of bioinformatics. It includes three categories: cellular component, molecular function, and biological process. GO functional annotation analysis results show the number of DEPs under each item in the three categories. GO functional enrichment analysis provides significant GO functional terms associated with the DEPs, that is, those biological functions significantly correlated with the DEP. In vivo, different proteins interact to generate a biological behavior, and a pathway-based analysis helps to further understand those biological functions. A significant pathway enrichment analysis can determine the most important biochemical and metabolic pathways and signal transduction pathways associated with the DEPs.

Table 1:
List of differentially expressed proteins in the early-onset preeclampsia group.
Regulated type Protein accession Gene name Protein description FC
UP A0A024R6R4 MMP2 Matrix metallopeptidase 2 21.48
UP A8K2X4 - cDNA FLJ75401 12.87
UP O00533 CHL1 Neural cell adhesion molecule L1-like protein 7.69
UP J3K000 PEPD PEPD protein 6.6
UP A8K3I0 - cDNA FLJ78437 6.31
UP Q6UXB8 PI16 Peptidase inhibitor 16 5.76
UP Q07954 LRP1 Prolow-density lipoprotein receptor-related protein 1 4.82
UP Q12860 CNTN1 Contactin-1 4.64
UP V9HWB4 HEL-S-89n Epididymis secretory sperm binding protein Li 89n 4.63
UP P35527 KRT9 Keratin, type I cytoskeletal 9 4.51
UP P14543 NID1 Nidogen-1 3.86
UP O95236 APOL3 Apolipoprotein L3 3.4
UP A6XNE2 - Complement factor D preproprotein 3.34
UP P41222 PTGDS Prostaglandin-H2 3.17
UP V9H1C1 - Gelsolin exon 4 (Fragment) 2.95
UP Q9Y5Y7 LYVE1 Lymphatic vessel endothelial hyaluronic acid receptor 1 2.95
UP Q13201 MMRN1 Multimerin-1 2.83
UP B3KQF4 - cDNA FLJ90373 2.74
UP Q6MZL2 DKFZp686M0562 Uncharacterized protein DKFZp686M0562 (Fragment) 2.71
UP J3KPA1 CRISP3 Cysteine-rich secretory protein 3 2.65
UP P01034 CST3 Cystatin-C 2.64
UP A0A0S2Z4F1 EFEMP1 EGF Containing Fibulin Extracellular Matrix Protein 1 2.62
UP P23142 FBLN1 Fibulin-1 2.57
UP A0A384N669 - Epididymis secretory sperm binding protein 2.48
UP J3KNB4 CAMP Cathelicidin antimicrobial peptide 2.47
UP B7Z544 - cDNA FLJ51742 2.43
UP A8K061 - cDNA FLJ77880 2.42
UP A0A087WV75 NCAM1 Neural cell adhesion molecule 1 2.37
UP Q9NQ79 CRTAC1 Cartilage acidic protein 1 2.24
UP B2RBW9 - cDNA, FLJ95746 2.23
UP A0A024RAA7 C1QC Adiponectin B 2.15
UP P16070 CD44 CD44 antigen 2.06
UP Q76LX8 ADAMTS13 Von Willebrand Factor-Cleaving Protease 2.01
UP Q6EMK4 VASN Vasorin 2
UP D6RE86 CP Ceruloplasmin (Fragment) 1.88
UP H6VRG1 KRT1 Keratin 1 1.87
UP Q86U17 SERPINA11 Serpin A11 1.81
UP Q9UKE5 TNIK TRAF2 and NCK-interacting protein kinase 1.75
UP P61626 LYZ Lysozyme C 1.74
UP P02743 APCS Serum amyloid P-component 1.74
UP Q16853 AOC3 Membrane primary amine oxidase 1.73
UP Q9NZP8 C1RL Complement C1r subcomponent-like protein 1.73
UP A0A024R035 C9 Complement C9 1.71
UP A0A0A0MRJ7 F5 Coagulation factor V 1.66
UP P09871 C1S Complement C1s subcomponent 1.65
UP Q6PIL8 IGK@ IGK@ protein 1.63
UP P49908 SELENOP Selenoprotein P 1.55
UP P02654 APOC1 Apolipoprotein C-I 1.55
UP D3DRR6 ITIH2 Inter-alpha (Globulin) inhibitor H2 1.54
UP B4DPQ0 C1R Complement C1r subcomponent 1.52
UP J3KNP4 SEMA4B Semaphorin-4B 1.51
DOWN Q6GMX6 IGH@ IGH@ protein 0.56
DOWN A0A0X9TD47 - MS-D1 light chain variable region (Fragment) 0.43
DOWN Q9UNU2 C4B Complement protein C4B frameshift mutant (Fragment) 0.43
DOWN P01591 JCHAIN Immunoglobulin J chain 0.34
DOWN Q14213 EBI3 Interleukin-27 subunit beta 0.34
DOWN P28799 GRN Granulins 0.33
DOWN B2R7N9 - cDNA, FLJ93532 0.32
DOWN P00709 LALBA Alpha-lactalbumin 0.29
DOWN G5E9F7 PSG1 Pregnancy specific beta-1-glycoprotein 1 0.25
DOWN A0A075B6A0 IGHM Immunoglobulin heavy constant mu (Fragment) 0.23
DOWN A0A075B6R9 IGKV2D-24 Immunoglobulin kappa variable 2D-24 0.23
DOWN O75636 FCN3 Ficolin-3 0.22
DOWN O43866 CD5L CD5 antigen-like 0.2
DOWN P06733 ENO1 Alpha-enolase 0.16
DOWN P09172 DBH Dopamine beta-hydroxylase 0.16
DOWN Q15485 FCN2 Ficolin-2 0.13
DOWN Q86TT1 Full-length cDNA clone CS0DD006YL02 of Neuroblastoma 0.08
DOWN P00739 HPR Haptoglobin-related protein 0.06
DOWN P00738 HP Haptoglobin 0.01
DOI: 10.7717/peerj.9753/table-1

The GO functional annotation analysis results for the EOPE group are shown in Fig. 4. For the biological process category, the highest percentages of the proteins were associated with the term biological regulation (n = 43 proteins; with the top three upregulated proteins in this process being CHL1, LRP1, and CNTN1). For the cellular component category, the highest percentages of the proteins were associated with the term extracellular region (n = 49 proteins; with the top 2 upregulated proteins in this process being MMP2 and CHL1). For the molecular function category, the highest percentages of the proteins were associated with the term binding (n = 45 proteins; with the top 3 upregulated proteins in this process being MMP2, CHL1, and PEPD). For the LOPE group vs. their controls, the highest percentages of the proteins in the biological process category were associated with the term biological regulation (n = 17; with the top 3 upregulated proteins in this process being PAPPA2, F7, and vWF). The highest percentages of the proteins in the cellular component category were associated with term extracellular region (n = 18; with the top three upregulated proteins in this process being PAPPA2, CETP, and F7). The highest percentages of the proteins in the molecular function category were associated with the term binding (n = 13; with the top 3 upregulated proteins in this process being APPA2, CETP, and F7). The terms in the GO functional enrichment analysis for the EOPE group vs. their controls mainly included response to stress, defense response, and negative regulation of catalytic activity, whereas the terms in the GO functional enrichment analysis for the LOPE group vs. their controls included enzyme inhibitor activity and serine-type endopeptidase inhibitor activity (Fig. 5).

KEGG signaling pathway analysis

The KOBAS online analysis tool was used to identify the functions associated with the DEPs and the KEGG signaling pathways. Results of the KEGG pathway enrichment analysis showed that complement and coagulation cascades, proteoglycans in cancer, and metabolic pathways were the main signaling pathways associated with the EOPE group, whereas complement and coagulation cascades were the main signaling pathways associated with the LOPE group (Fig. 6). Thus, the results indicated that a functional change in coagulation was the main finding for both EOPE and LOPE.

Differential protein expression.

Figure 3: Differential protein expression.

(A and C) Volcano plots with red dots on the right-hand side indicating upregulation, green dots 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 log10 fold change and P < 0.05 between early-onset preeclampsia and its control group (A), and between late-onset preeclampsia and its control group (C). (B and D) Clustergram for the expression of the DEPs between early-onset preeclampsia and control (B) and between late-onset preeclampsia and control (D).
Table 2:
List of differentially expressed proteins in the late-onset preeclampsia group.
Regulated type Protein accession GENE NAME PROTEIN DESCRIPTION FC
UP Q9BXP8 PAPPA2 Pappalysin-2 5.27
UP A0A0S2Z3F6 CETP Cholesteryl ester transfer protein plasma isoform 1 3.51
UP P08709 F7 Coagulation factor VII 2.8
UP Q6NS95 IGL@ IGL@ protein 2.67
UP P04275 VWF von Willebrand factor 2.46
UP C0JYY2 APOB Apolipoprotein B 1.93
UP A0A140VK24 - Testicular secretory protein Li 24 protein 1 1.74
UP P05546 SERPIND1 Heparin cofactor 2 1.52
UP O95445 APOM Apolipoprotein M 1.34
UP P80108 GPLD1 Phosphatidylinositol-glycan-specific phospholipase D 1.26
DOWN P05543 SERPINA7 Thyroxine-binding globulin 0.82
DOWN Q9NZP8 C1RL Complement C1r subcomponent-like protein 0.63
DOWN A8K2T7 - Receptor protein-tyrosine kinase 0.61
DOWN Q00887 PSG9 Pregnancy-specific beta-1-glycoprotein 9 0.6
DOWN P07333 CSF1R Macrophage colony-stimulating factor 1 receptor 0.56
DOWN Q2L9S7 AAT Alpha-1-antitrypsin MBrescia variant receptor 1 0.53
DOWN A0A2S0BDD1 ATIII-R2 Antithrombin III isoform 0.49
DOWN Q8WW79 SELL L-selectin 0.48
DOWN P40189 IL6ST Interleukin-6 receptor subunit beta (Fragment) 0.47
DOWN B2R7Y0 - cDNA, FLJ93654 0.41
DOWN Q12860 CNTN1 member 2 (SERPINB2) 0.36
DOWN Q8WWZ8 OIT3 Oncoprotein-induced transcript 3 protein 0.31
DOWN A0A140TA29 C4B Complement C4-B 0.21
DOWN B2R950 - cDNA, FLJ94213 0.18
DOI: 10.7717/peerj.9753/table-2
Gene ontology functional annotation.

Figure 4: Gene ontology functional annotation.

The upregulated and downregulated proteins in the three categories of biological process, cellular component, and molecular function in (A) the early-onset preeclampsia group vs. its control group and (B) the late-onset preeclampsia group vs. its control group.
Gene ontology (GO) functional enrichment.

Figure 5: Gene ontology (GO) functional enrichment.

Enriched GO terms for the upregulated and downregulated proteins and the associated protein numbers in (A) early-onset preeclampsia vs. its control and (B) late-onset preeclampsia vs. its control.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.

Figure 6: Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment.

KEGG pathways enriched for the upregulated and downregulated proteins and the associated protein numbers in (A) early-onset preeclampsia vs. its control and (B) late-onset preeclampsia vs. its control.

PSG9 protein level in serum

Serum level of PSG9 was measured by ELISA. The data showed a significant difference between LOPE group and control group (4.54 ± 1.22 vs. 6.32 ± 1.73, Fig. 7). The result is consistent with the study of mass spectrometry.

Protein level of pregnancy-specific beta-1-glycoprotein 9 (PSG9) in serum.

Figure 7: Protein level of pregnancy-specific beta-1-glycoprotein 9 (PSG9) in serum.

Concentration of PSG9 in the patient serum in control and LOPE groups. Values are shown as the mean ±SEM (n = 10); *P < 0.05 for Control vs. LOPE.


Proteomics methods have provided some important information regarding PE. For example, Blumenstein et al. (2009) used a differential in gel electrophoresis–based approach to identify changes in the plasma proteome of pregnant women who subsequently developed PE. They found that those DEPs are mainly involved in lipoprotein metabolism, the blood coagulation system, and the complement system. The results of the present study were consistent with those of Blumenstein et al. (2009), but we also made some new discoveries (discussed below). These results suggest that the pathogenesis of PE is complex and is associated with multiple proteins and signaling pathways.

To maintain pregnancy and fetal growth progress within normal reference ranges, healthy pregnant women develop physiological hyperlipidemia. However, they do not experience angiopathy because high-density lipoprotein (a vascular protection factor) and low-density lipoprotein (LDL, an atherogenic factor) also increase to protect the vascular endothelium from injury (Jin et al., 2016; Wang et al., 2017). Compared with healthy pregnant women, pregnant women with PE display significantly increased serum triglycerides and LDLs, which may enhance oxidative stress and ultimately lead to vascular endothelial cell injury (Huda, Sattar & Freeman, 2009; Pohanka, 2013). Our study found that there were six DEPs associated with lipid regulation increased in the PE groups, including ApoB, LRP1, ApoL3, ApoC-I, CETP, and ApoM. ApoB, the main carrier protein of LDL, is an atherogenic risk factor. Previous evidence supports that ApoB is significantly upregulated in the plasma of patients with PE (Lin et al., 2019). CETP plays an important role in high-density lipoprotein metabolism and reverse cholesterol transport and is upregulated in the third trimester of pregnancy. Another study has reported that the TaqIB polymorphism of the CETP gene is significantly correlated with the triglyceride and total cholesterol levels in patients with severe PE (Belo et al., 2004). Here, we reported for the first time, to our knowledge, that LRP1, ApoC1, and ApoL3 are increased in the EOPE group. LRP1 and ApoC1 accelerate the development of atherosclerosis through different pathways (Mueller et al., 2018; Westerterp et al., 2007). Endothelial function decreases during PE, and endothelial dysfunction is a characteristic of atherosclerosis. ApoL3, as a regulator of MAPK and FAK signaling in endothelial cells, has been shown to be involved in angiogenesis in vitro (Khalil et al., 2018). Increased ApoL3 in PE may be a compensatory response to endothelial dysfunction. Therefore, the current evidence suggests that dyslipidemia may be related to the development of PE, but the specific pathogenesis remains to be explored in future studies.

Physiological hypercoagulability in healthy pregnant women can prevent intrapartum and postpartum hemorrhage. However, abnormalities in coagulation, the anti-coagulation system, and the fibrinolytic system lead to pathological hypercoagulability in PE (Dusse et al., 2011). In the LOPE group of the present study, coagulation factors VII, vWF, and SRPIND1 were upregulated group, whereas AAT and ATIII were downregulated compared with healthy controls. In the EOPE group, coagulation factor V, plasminogen, and ADAMTS13 were upregulated. These results are consistent with those of many previous trials (discussed below). The level of plasma FVII in patients with severe PE is significantly higher than that in healthy pregnant women; therefore plasma FVII levels may show high sensitivity and specificity in differentiating between PE and normal pregnancy (Dusse et al., 2016). The coagulation factor vWF is a specific marker that reflects damage of endothelial cells; thus, damaged microvascular endothelial cells in PE promote the expression of vWF. Owing to the activation of the intrinsic and exogenous coagulation pathways by damaged endothelial cells, a large number of coagulation factors are activated. This leads to a massive generation of thrombin, which antagonizes a large amount of anti-thrombin III, resulting in a significant decrease in its level (Demir & Dilek, 2010; Pottecher et al., 2009). In response to abnormalities in the coagulation mechanism of PE, evidence-based medical study has also shown that oral administration of low-dose aspirin during early pregnancy can significantly reduce the incidence rate of PE (Rolnik et al., 2017).

A growing number of studies have shown that abnormal expression with the complement system is associated with PE (Agostinis et al., 2010; Derzsy et al., 2010). Our results support this evidence, showing that expression levels of CFD, CIQC, CIRL, C9, C1S, and C1R were significantly increased, while expression levels of C4B, FCN2, and FCN3 were significantly decreased in the EOPE group compared with their control group. Moreover, the expression levels of complement C1RL and C4B were decreased in the LOPE group compared with their controls.

Downregulation of PSG1 and PSG9 expressed in the PE groups is an interesting finding in our study. PSG is a pregnancy-specific glycoprotein that is synthesized and secreted into the blood by placental syncytiotrophoblast cells. Rong et al. (2017) found that PSG9 significantly promotes the angiogenesis of human umbilical vein endothelial cells. Chang et al. (2016) found a significant increase in the number of cases with deletions in the PSG gene locus among patients with PE. We hypothesize that downregulation of the PSG protein family may be involved in the pathogenesis of PE by affecting the proliferation of endothelial cells although no specific experimental evidence currently supports or disproves this hypothesis.

PE is considered a placenta-derived disease. In the classic two-stage model, placental stress leads to dysfunction of maternal peripheral endothelial cells, systemic inflammatory response, and the clinical syndrome of PE (Staff, 2019). This model is also reflected in our mass spectrometry results. In both the EOPE and LOPE groups, abnormalities of coagulation cascade activation, lipid metabolism, and complement activation were found. However, the main stressor associated with EOPE is placental hypoperfusion secondary to impaired spiral artery remodeling; by contrast, in LOPE, the cause is more likely attributable to a mismatch between normal maternal perfusion and the metabolic demands of the placenta and fetus (Burton et al., 2019). Spiral artery remodeling is completed by the placental trophoblast cells continuously invading and destroying the uterine spiral arterial wall and gradually replacing the endothelial cells. Many studies have focused on the control of this invasion (Pollheimer et al., 2018; Wagner, Otomo & Christians, 2011; Wu et al., 2016).

Unexpectedly, we also found 19 DEPs in the EOPE group that were closely related to placental development or invasion of tumor cells. This result was not detected in the LOPE group, which showed an increase only in PAPPA2, which affects the invasion and metastasis of placental trophoblasts and leads to a gradual decline in placental function (Wagner, Otomo & Christians, 2011). LYVE1, CST3, and NCAM1 are also reportedly involved in placental vascular remodeling (Pawlak et al., 2019; Song et al., 2010; Zhang, Xu & Han, 2019). GRN, CD5L, ENO1, CHL1, CRISP3, VASN, and TNIK promote the invasive ability of tumor cells (Aran et al., 2018; Bhandari et al., 2019; Buhusi et al., 2003; Chon et al., 2016; Song et al., 2014; Voshtani et al., 2019; Wang et al., 2019). By contrast, SEMA4B, KRT1, KRT9, FBLN1, and PTGDS are involved in the anti-invasive activity of tumor cells (Blanckaert et al., 2015; Jian et al., 2015; Marano et al., 2018; Zhang et al., 2018). MMP2 plays an important role in trophoblast invasion and is generally thought to be downregulated in PE (Wu et al., 2016). We found the opposite result, that is, the expression of MMP2 was upregulated in EOPE; however, expression of PI16, which inhibits MMP2 activity (Hazell et al., 2016), was also upregulated. PEPD is a hydrolase that affects collagen biosynthesis, cell proliferation, and matrix remodeling. Pehlivan et al. (2017) have shown that PEPD activity in the plasma, umbilical cord, and placental tissue of women with pregnancy-induced hypertension is higher than that of healthy pregnant women. Our results also indicated an upregulation of PEPD in the EOPE group. The dysregulation of the expression of these proteins may be an important cause of placental dysfunction in EOPE.

Furthermore, previous studies have shown that soluble form of placenta-derived endoglin (sENG) was upregulated in the serum of EOPE, which is involved in endothelial dysfunction in coordination with soluble fms-like tyrosine kinase (Venkatesha et al., 2006). Our mass spectrometry results were similar to that previous finding showing that endoglin was upregulated in EOPE but not in LOPE group. This finding may also indicate more severe endothelial dysfunction in EOPE.


In summary, the use of iTRAQ combined with LC-MS/MS was effective for screening serum for DEPs in PE. We used bioinformatics to analyze the DEPs that showed significant changes in their expression levels to provide potential indicators for detecting PE and ideas for the study of pathogenesis in PE. In conclusion, the pathogenesis of EOPE and LOPE appeared to be related to dysfunctions in coagulation cascade activation, lipid metabolism, and complement activation. However, compared with that in LOPE, the pathogenesis of EOPE was associated with greater placental injury. This study provides several new proteins in the serum which may potentially have value in clinical diagnosis of EOPE and LOPE and offers potential mechanisms for the development of EOPE and LOPE, contributing to the future research on prediction, prevention, and pathogenesis of PE.

Supplemental Information

Compared with serum samples from healthy pregnant women, those from women with EOPE displayed 70 proteins that were differentially expressed with significance

Among them, 51 proteins were significantly upregulated and 19 proteins were significantly downregulated.

DOI: 10.7717/peerj.9753/supp-1

In serum samples from women with LOPE, 24 DEPs were identified, with 10 proteins significantly upregulated and 14 proteins significantly downregulated compared with healthy pregnant women

DOI: 10.7717/peerj.9753/supp-2
6 Citations   Views   Downloads