Maternal plasma proteome profiling of biomarkers and pathogenic mechanisms of early-onset and late-onset preeclampsia

doi:10.21203/rs.3.rs-1429136/v1

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Maternal plasma proteome profiling of biomarkers and pathogenic mechanisms of early-onset and late-onset preeclampsia

https://doi.org/10.21203/rs.3.rs-1429136/v1

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Background

Preeclampsia is still the leading cause of morbidity and mortality in pregnancy, however there are no current effective therapeutic strategies. This has been impeded by poorly understood pathogeneses of preeclampsia and its multifactorial and heterogeneous nature. Two phenotypes of preeclampsia have been characterised based on the time of diagnosis, early-onset (EOPE, before 34 weeks’ of gestation) and late-onset (LOPE, from 34 weeks’ of gestation). However, the molecular differences between these two phenotypes are not fully elucidated.

Objectives

This study aimed to facilitate better understanding of the mechanisms of pathogenesis of EOPE and LOPE, and identify specific biomarkers of preeclampsia.

Methods

In this study, we conducted an untargeted proteomic analyses of plasma samples from pregnant women with EOPE (n=17) and LOPE (n=11), and age, BMI-matched normotensive controls (n=18).

Results

In total, there were 26 and 20 differentially abundant proteins between EOPE or LOPE, and normotensive controls, respectively. Only inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3) was differentially abundant and 11 proteins were commonly shared between EOPE and LOPE. A series of angiogenic proteins, including insulin-like growth factor-binding protein 4 (IGFBP4; EOPE: FDR =0.30 x 10^-3 and LOPE: FDR =3.96 x 10^-3) and histidine-rich glycoprotein (HRG), were significantly perturbed in both phenotypes (EOPE: FDR=7.8 x 10^-3; LOPE: FDR =4.1 x 10^-3).

Conclusions

Overall, proteins associated with lipid metabolism were the key proteins perturbed in EOPE, however, ECM proteins had a more pronounced role in LOPE. The homeostasis-related pathway including platelet activation, signalling and aggregation were more perturbed in EOPE than LOPE.

proteomics

preeclampsia

early-onset

late-onset

Hao Chen reports Australian Government Research Training Program (RTP) Stipend and RTP Fee-Offset Scholarship through University of Technology Sydney. Other authors have nothing to disclose.

Hypertension in pregnancy is the leading cause of morbidity and mortality in pregnancy, affecting up to 10% of all gestations¹. Hypertensive disorders of pregnancy include chronic hypertension, gestational hypertension, eclampsia and pre-eclampsia, all of which increase the risk of complications in both mothers and babies during gestation. Preeclampsia, in particular, is characterised by the new-onset of gestational hypertension in the presence of proteinuria or other organ damage. It affects 5–7% of pregnancies and causes approximately 76,000 maternal deaths and 500,000 foetal deaths worldwide each year².

The classification of preeclampsia has been evolving over the last decade. In 2013, the American College of Obstetricians and Gynecologists (ACOG) and International Society for the Study of Hypertension in Pregnancy (ISSHP) included other symptoms/features including liver dysfunction, thrombocytopenia, cerebrovascular events or foetal growth restriction (FGR), in the absence of proteinuria, to diagnose preeclampsia^1,3,4. Despite the fact that preeclampsia is a multifactorial and heterogeneous disorder, it is now widely accepted that preeclampsia is stratified depending on the time of onset into: i) early-onset preeclampsia (EOPE) manifested before 34 weeks of gestation, and ii) late-onset preeclampsia (LOPE) manifested from 34 weeks of gestation. Although EOPE and LOPE share the same diagnostic criteria, these two phenotypes of preeclampsia lead to different outcomes. EOPE is commonly associated with FGR, abnormal uterine artery Doppler often leading to preterm birth and higher risk of post-pregnancy morbidities^5,6. On the other hand, LOPE appears to be a less severe disorder, often with normal or slightly increased uterine resistance index and a low rate of FGR^6,7. Distinct delineation between EOPE and LOPE is still not well understood, with most patients with preeclampsia presenting elements of both pathologies proposing a clinical spectrum.

The lack of untargeted discovery studies involving ‘omics’ analyses has impeded understanding of the molecular differences between these two phenotypes of preeclampsia. Notably, a study from 2005⁸ utilised a proteomics approach using urine samples from a cohort of pregnant women with EOPE and healthy controls, however, the differences between EOPE and LOPE were not elucidated. Another more recent bioinformatics study, identified overlapping pathogenic mechanisms between preeclampsia, hypertension and heart disease but could not stratify between EOPE and LOPE due to underreporting of the data related to individual phenotypes⁹. In this study, we conducted an unbiased, comprehensive proteomics analysis using plasma samples collected from patients with EOPE (n = 17) and LOPE (n = 11), compared with age- and BMI-matched normotensive controls (n = 18). The use of plasma samples is able to better reflect the pathogenesis of EOPE and LOPE as systemic conditions centred by widespread endothelial dysfunction¹⁰.

Patient characteristics

Clinical characteristics of the participants are presented in Table 1. The distribution of age and BMI were similar across EOPE, LOPE and healthy pregnancy groups, whereas GA at delivery was significantly lower in EOPE (31.3 ± 2.5 weeks) and LOPE (37.03 ± 3.4 weeks), compared to healthy pregnancy groups (39.4 ± 5.5 weeks; p˂0.05), in line with the frequent earlier delivery in EOPE compared to LOPE or healthy controls¹⁸. Maternal blood pressure was higher in EOPE (156.4 ± 26.5) and LOPE (144.4 ± 16.8) compared to healthy pregnancies (113.9 ± 8.5), p˂0.05. Heart rate was also increased in women with EOPE (94.6 ± 29.8) compared to healthy controls (74.4 ± 6.3, p˂0.05).

Plasma proteins differentially abundant between different phenotypes of preeclampsia, and compared to healthy controls

Quantitative proteomic analysis of plasma samples was conducted by measuring tryptic peptides using data-dependent acquisition (DDA) mass spectrometry. The generated outputs contained 370 proteins detected among all 31 samples with >85% data completion (Supplementary data 1).

Initially, the clustering of groups was assessed through observing the principal component analysis (PCA) plot (Fig. 2a), which is consistent with the unsupervised hierarchical clustering of heatmap (Fig. 2b). Both PCA and heatmap revealed that the proteomes are heterogeneous between EOPE, LOPE and healthy pregnancy groups. Following PCA, the differential expression analysis was performed by three separate comparisons, including i) EOPE vs healthy pregnancy, ii) LOPE vs healthy pregnancy and iii) EOPE vs LOPE, while adjusting for GA at delivery. In total, there were 26 and 20 differentially abundant proteins in EOPE vs healthy pregnancy and LOPE vs healthy pregnancy, respectively, and one protein was differentially expressed between EOPE and LOPE (Fig. 2c-e; Fig. 3a; Supplementary data 2).

Overall, the differences in proteome profiles between EOPE or LOPE and healthy pregnancy were largely associated with impaired angiogenesis, and included perturbance in the abundance of inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3)¹⁹, insulin-like growth factor-binding protein 4 (IGFBP4)^20–23 and histidine-rich glycoprotein (HRG)^24–26 (Fig. 3a), proteins. Immunoglobulin heavy variable 1/OR15-1 (IGHV1OR15-1), the only protein differentially abundant between two subtypes of preeclampsia, was dramatically overexpressed in EOPE compared to LOPE (FC =14.29, FDR =4.86 x 10^-3) (Fig. 3b).

Furthermore, there were 11 proteins commonly shared between EOPE and LOPE. Immunoglobulin lambda variable 3-21 (IGLV3-21) was the top differentially abundant protein shared between EOPE (FDR =2.28 x 10^-3) and LOPE (FDR =3.96 x 10^-3) with approximately 50% decrease in its abundance compared to healthy pregnancy. Well-characterised preeclampsia biomarkers including fibronectin 1 (FN1)^27,28 and complement factor D (CFD)^29,30, were ~2-fold increased and among the most significant abundant proteins in both EOPE and LOPE. HRG is another well-studied biomarker of preeclampsia^31–33 that was increased ~2-fold in both phenotypes of preeclampsia (EOPE: FDR=7.8 x 10^-3; LOPE: FDR =4.1 x 10^-3). Interestingly, there were two different ITIH proteins, differentially abundant in each preeclampsia subtype, where ITIH3 was increased in EOPE (FC =1.60, FDR =1.18 x 10^-2), and ITIH2 was increased in LOPE (FC =1.29, FDR =3.30 x 10^-2).

In line with the Venn diagram, the proteins with outstanding perturbations were highlighted in a three-dimensional plot (Fig. 3b; Supplementary data 3). Among the proteins with significant fold changes, a series of proteins were previously reported as biomarkers of preeclampsia, including serpin family A member 5 (SERPINA5)³⁴, pappalysin 2 (PAPPA2)^35,36, hepatocyte growth factor activator (HGFAC)^37,38 and thymosin beta-4 (TMSB4X)³⁹. IGFBP4, a protein significantly overexpressed in EOPE (log₂FC=4.25, FDR =0.30 x 10^-3) and LOPE (log₂FC=4.91, FDR =3.96 x 10^-3), with anti-angiogenic properties^20–23, confirming the central role of impaired angiogenesis in preeclampsia.

Pathogenic pathway associated with different phenotypes of preeclampsia

Following identification of differentially abundant biomarkers of different phenotypes of preeclampsia, pathway enrichment was performed in EOPE or LOPE group, compared to healthy pregnancy with annotation by Reactome database¹⁴. Pathways altered in EOPE and LOPE (FDR <0.05) were presented using a triple Venn diagram (Fig. 4a; Supplementary data 4). The pathway Venn diagram revealed a range of pathways associated with altered haemostasis and immune system.

A total of 13 pathways were significantly different, with 6 pathways shared between EOPE and LOPE groups (Fig. 4a). We demonstrated the perturbations of differentially abundant proteins in the biological pathway regulating insulin-like growth factor (IGF) transport and uptake through IGF binding protein (IGFBP; Fig. 4b), which plays a significant role in both EOPE (FDR =9.90 x 10^-5) and LOPE (FDR =1.69 x 10^-3). Given the pro-angiogenic function of IGF signalling pathway^40,41, our findings emphasised the importance of impaired angiogenesis in the pathophysiology of preeclampsia. In addition, we also observed a series of perturbed haemostatic pathways, particularly platelet degranulation in response to the elevated intra-platelet Ca²⁺ being more pronounced in EOPE (FDR =1.42 x 10^-5; Supplementary data 4). EOPE was more closely associated with inflammatory profile than LOPE, due to the presence of a number of unique pathways of acute inflammation.

Signalling networks in EOPE and LOPE

Pairwise correlation network analysis was next performed to investigate protein-protein interactions (PPIs) in EOPE and LOPE. Networks were highlighted with the most correlated nodes (Pearson correlation r >0.7 or <-0.7), where the colour and the length of the edge are proportional to the Pearson correlation coefficient r (Fig 4a&b). To compare the PPIs in our data with broad reference evidence, the edge width was coded proportionally to the PPI-confidence scores derived from the STRING database¹⁵. Therefore, if consistent with the references, a pair of nodes are presented with a thick and opaque edge in between, or vice versa. Overall, our findings in terms of PPIs were consistent with those reported by broad evidence (Fig. 5a and 5b; Supplementary data 5).

To aid the readability of networks, the size of each node was programmed proportional to the corresponding eigencentrality (the influence in the entire network). Additionally, we also annotated the functional classification of each protein using DAVID^16,17, as indicated by the node colours.

As showed in the networks, all pairwise correlations were positive. The PPI network in EOPE is simple, suggesting a more direct pathogenesis in EOPE than LOPE (Fig. 5a and 5b). Proteins associated with lipid metabolism and extracellular matrix (ECM) were the key proteins perturbed in EOPE, and ECM proteins appear to play a more important role in LOPE. ITIH3 and APOC4-APOC2 were identified as having a substantial influence within the PPI network in EOPE. LOPE was more closely associated with a range of ECM proteins including von Willebrand factor (vWF), IGFBP4 and ITIH2.

Systemic angiogenic imbalance in the maternal circulation is one of the most well-characterised pathogenic mechanisms in preeclampsia^42,43. Although the complications and morbidities associated with the two phenotypes of preeclampsia are substantially different, the molecular insights underlying these differences are currently lacking. Using plasma samples from age- and BMI-matched patients with EOPE or LOPE and normotensive women with healthy pregnancy, we identified a core set of plasma biomarkers and signalling pathways in EOPE and LOPE.

The systemic biomarkers commonly utilised clinically for short-term prediction and diagnosis of preeclampsia include markers of angiogenesis, soluble fms-like tyrosine kinase-1 (sFlt1) and placental growth factor (PIGF)⁴⁴. In line with the importance of angiogenesis in the pathogenesis of preeclampsia, we discovered a series of angiogenic proteins⁴⁴, including ITIH3¹⁹, IGFBP4^20–23 and HRG^24–26, all of which were among the most significantly perturbed proteins in both phenotypes of preeclampsia. Interesting, these angiogenesis-associated proteins were perturbed to a higher extent in EOPE than LOPE. This is consistent with Schaarschmidt et al.’s study⁴⁵, where the progressive increase of sFlt1 and sFlt1/PIGF ratio was more prominent in EOPE than LOPE. Therefore, it is likely that higher levels of these biomarkers are more representative of EOPE, which should be explored in the future. Other angiogenesis biomarkers recently identified, FKBPL and CD44, appear to be more closely associated with LOPE⁴⁶. Our study is one of the first studies to provide molecular insights into the difference of angiogenic pattern between EOPE and LOPE at the proteome systemic level.

Our comprehensive proteomics analysis identified the angiogenic IGF signalling pathway, as perturbed in both EOPE and LOPE. There is strong evidence supporting the role of this regulatory pathway in maternal circulation in preeclampsia⁴⁷. Dysregulation of IGF pathway plays a role in inadequate trophoblast invasion of the maternal decidua, resulting in inappropriate placentation⁴⁸. Considering the importance of the angiogenic balance, our findings provide a mechanistic insight into the complex interplay between pro- and anti-angiogenic factors of IGF signalling pathway in preeclampsia. IGFBP4, one of the core components of the IGF signalling pathway, was substantially up-regulated in both EOPE and LOPE. The elevation of IGFBP4 levels is reflective of restrictive angiogenesis^21,22. PAPPA2, a protease of IGFBP5⁴⁷, was also highly overexpressed in EOPE compared to LOPE. Although a number of studies reported up-regulation of PAPPA2 in maternal circulation as a result of preeclampsia^49–51, we demonstrated the specific increase of PAPPA2 levels in EOPE. We did not identify IGFBP5 in our plasma proteomics analysis, which could be due to the excessive cleavage by overexpressed PAPPA2. Likewise, the increase of PAPPA2 levels in maternal circulation may compensate for the suppression of IGF signalling pathway due to up-regulated IGFBPs, which was also suggested by Nishizawa et al.’s study⁵¹.

On the other hand, APOE was up-regulated in both phenotypes of preeclampsia. APOE is well-known for its protective role in atherosclerosis, and APOE-knockout (KO) is often used in pre-clinical atherosclerotic models⁵². Interestingly, multiple in vivo studies suggested that IGFBP4 could also have a protective role in atherosclerosis^53–57. For example, when PAPPA, the protease of IGFBP4, was transgenically overexpressed in atherosclerotic models (APOE-KO), IGF bioavailability was enhanced due to reduced IGFBP4, resulting in a more severe atherosclerotic plaque formation⁵⁶. Lipid deposition commonly occurs in spiral uterine arteries in preeclampsia, resembling early stage of atherosclerosis⁵⁸. In our findings, the pathways associated with atherosclerosis were more pronounced in EOPE than LOPE, particularly platelet activation, signalling and aggregation. The different extents of atherosclerotic pattern may explain the more severe symptoms and higher risk of future cardiovascular events in EOPE⁵⁹.

In this study, we discovered two novel ITIH protein biomarkers of preeclampsia, which have not been reported previously, showing ITIH3 to be increased in EOPE, and ITIH2 in LOPE. Exacerbated maternal systemic inflammation is crucial in the pathophysiology of both phenotypes of preeclampsia^60–62. ITIH proteins play a contradictory role in the setting of inflammation, acting as both pro- and anti-inflammatory acute-phase protein⁶¹. The most well-known function of ITIH proteins is linked to their capability of inhibiting tumour growth and metastasis, which is largely dependent on their ability to stabilise ECM and their covalent binding to hyaluronic acid (HA), a pro-angiogenic ligand when degraded^63,64. The elevation of ITIH proteins may indicate an inhibitory effect on degradation of ECM and HA-CD44 pathway, which in turns leads to anti-angiogenic effect^63,64. Recently, a study demonstrated predictive and diagnostic biomarker potential of a novel anti-angiogenic FKBPL pathway via CD44, in preeclampsia⁴⁶. Specific elevation of ITIH3 and ITIH2 in EOPE and LOPE, respectively, likely reflect molecular differences related to the pathogenesis of these different phenotypes of preeclampsia that could be explored in the future as specific systemic biomarkers of EOPE or LOPE.

In conclusion, the plasma proteome signatures reflective of preeclampsia revealed perturbations in a series of angiogenesis-associated proteins and signalling pathways. The IGF signalling pathway appears to play a key role in regulating the systemic angiogenic imbalance in preeclampsia. The atherosclerosis-associated pathways were more closely linked to EOPE than LOPE, highlighting the significant contribution of vascular haemostatic instability in EOPE, which is diagnosed before 34 weeks of gestation. We also discovered two ITIH proteins, specific for different phenotypes for preeclampsia that could be explored in the future for personalised management of preeclampsia. Overall, our untargeted and comprehensive study of plasma proteome profiling identified a number of novel biomarkers and pathogenic pathways in preeclampsia, specifically emphasising differences between EOPE and LOPE and the importance of perturbed angiogenesis and inflammation in this condition.

Patient recruitment and sample collection

Blood samples were collected from healthy normotensive pregnant women (n=18) and age and body mass index (BMI)-matched women diagnosed with EOPE (n=17) or LOPE (n=11). Diagnosis of preeclampsia was carried out according to the 2013 ACOG guidelines¹. EOPE was diagnosed before 34 weeks of gestation whereas LOPE was diagnosed from 34 weeks of gestation. Blood sampling took place upon hospital admission for delivery. Blood was collected in EDTA coated tubes. Following collection, the samples were centrifuged at 3,000 rpm for 10 minutes at 4°C to collect plasma. Pregnant women with multiple pregnancies, pre-existing hypertension, diabetes mellitus, cardiovascular and renal conditions complicated with proteinuria, were excluded from the study.

All participants provided written informed consents prior to inclusion in the study. The study was approved by the local institutional human ethics review boards including University of Technology Sydney, University of Nis and all the local hospital human ethics committees in accordance with the Declaration of Helsinki and the National Statement on Ethical Conduct in Human Research (Australia).

Proteomics

All plasma samples were thawed and kept on ice during the entire proteomic experiment. Plasma sample preparation was adjusted from the previously described methods, stop-and-go-extraction tips (StageTips)¹¹. The overview of the study and experimental design as well as data analysis is depicted in Fig. 1. Briefly, 1 μL (~50-60 μg peptides determined by BCA assay) of plasma samples were resuspended in lysis buffer composed of 50 mM Tris-HCl pH 8.8, 1% sodium deoxycholate, 5 mM Tris (2-carboxyethyl) phosphine hydrochloride and 20 mM acrylamide monomer, at a sample to buffer ratio of 1:25, and heated to 95°C for 10 min for denaturation, reduction and alkylation. The lysates were diluted 1:9 with water. Proteins were digested in sequencing grade trypsin (1 μg/μL; Promega, USA) overnight at 37°C, at an enzyme to substrate ratio of 1:250. The digestion was ceased by equal volume of ethyl acetate containing 1% trifluoroacetic acid (TFA), followed by settling until two phases appeared.

The peptide mixtures were then cleaned, concentrated and enriched through StageTips. The StageTip was made by a core extracted from a 47 mm styrene divinyl benzene-reversed phase sulfonate disk using a 14G flat-ended needle. The core was then inserted into an ordinary 100 μL pipette tip from the top until it reached the maximal depth. Prior to sample addition, the tips were cleaned with 100 μL 100% acetonitrile and centrifuged at 1,000 x g for 1 min. The tips were equilibrated with 100 μL 0.1% TFA in water and 30% methanol/1% TFA and centrifuged at 1,000 x g for 3 min. The two phases of each sample were mixed by vigorous vortex, followed by an extraction of the volume equivalent of ~10 μg digested peptides loaded onto each StageTip. The loaded peptides were washed twice by 100 μL 99% ethyl acetate/1% TFA, followed by a wash with 100 μL 0.2% TFA in water. To elute, 100 μL 5% ammonium hydroxide/80% acetonitrile was added to each tip and centrifuged at 1,000 x g for 5 minutes into an autosampler vial (Thermo Fischer, USA). Samples in the vials were dried in SpeedVac vacuum concentrator (Thermo Fisher) at highpower for 1 hour. The peptides were then resuspended in 25 μL 0.1% formic acid.

The setup of mass spectrometry was previously described¹². Using an Acquity M-class nanoLC system (Waters, USA), 5 µL of the sample was loaded at 15 µL/min for 3 mintues onto a nanoEase Symmetry C18 trapping column (189 µm x 20 mm) before being washed onto a PicoFrit column (75 µmID x 300 mm; New Objective, USA) packed with Magic C18AQ resin (3 µm;Michrom Bioresources, CA). Peptides were eluted from the column and into the source of an Q Extractive^TM Plus mass spectrometer (Thermo Fischer) using the following program: 5-30% MS buffer B (98% acetonitrile + 0.2% formic acid) over 90 minutes, 30-80% MS buffer B over 3 minutes, 80% MS buffer B for 2 minutes, 80-5% for 3 minutes. The eluting peptides were ionised at 2400V. A Data Dependant MS/MS (dd-MS²) experiment was performed, with a survey scan of 350-1500 Da performed at 70,000 resolution for peptides of charge state 2⁺ or higher with an AGC target of 3e6 and maximum injection time of 50 ms. The top 12 peptides were selected fragmented in the HCD cell using an isolation window of 1.4 m/z, an AGC target of 1e5 and maximum injection time of 100 ms. Fragments were scanned in the Orbitrap analyser at 17,500 resolution and the product ion fragment masses measured over a mass range of 120-2000 Da. The mass of precursor peptides was then excluded for 30 seconds.

The MS/MS data files were searched using PEAKS Studio X+ (Bioinformatics Solutions, CA) against the PEAKS DB human proteome database (2019) and a database of common contaminants (2019) with the following parameter settings: fixed modifications =none; variable modications =propionamide, oxidised methionine, deamidated asparagine; enzyme =semi-trypsin; number of allowed missed cleavages =3; peptide mass tolerance =10 ppm; MS/MS mass tolerance =0.05 Da. The results of the search were then filtered to include peptides with a -log₁₀P score that was determined by the False Discovery Rate (FDR) of <1%, in which decoy database search matches were <1% of the total matches.

Statistics

All statistical analyses were performed in R (4.0.2). All proteomic data were total ion count (TIC)-normalised, followed by log₂-transformed prior to entering pipelines in R. The packages or functions utilised in this study included: i) limma package¹³ for DE analysis of comparisons between EOPE, LOPE and healthy pregnancy groups, while adjusting for gestational age (GA) at delivery, ii) geneSetTest function in limma package for pathway analyses annotated by Reactome database¹⁴, and iii) igraph package for network analyses of protein-protein interactions (PPIs) annotated by pairwise Pearson correlation, PPI scores in STRING database¹⁵ and protein functions in DAVID database^16,17. Relative quantification of proteins or pathways between subtypes was expressed as fold change (FC). The Benjamini-Hochberg method was used to calculate the adjusted P values, also known as FDR, for all aforementioned analyses. DE proteins were defined as FDR <0.05.

Data Availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD026931. Also, the authors provided a publicly available, interactive online application of supporting data for easy navigation (https://hao-chen-uts-99171821.shinyapps.io/PEplasmaproteomics/_w_1022d2ef/). Username and password can be obtained from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

All participants provided written informed consents prior to inclusion in the study. The study was approved by the local institutional human ethics review boards in accordance with the Declaration of Helsinki and the National Statement on Ethical Conduct in Human Research (Australia).

Acknowledgements

We thank the participants for taking part in this study and all healthcare staff for helping with the recruitment of the participants. The authors would like to acknowledge the use of BioRender for the creation of the schematics used in figures.

Conflict of Interest

The authors declare no conflict of interest.

Funding

The funding was provided by the Faculty of Science, University of Technology Sydney, Australia.

Hao Chen was supported by an Australian Government Research Training Program (RTP) Stipend and RTP Fee-Offset Scholarship through University of Technology Sydney.

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Table 1. Summary of patient characteristic.

Characteristics	EOPE (n=17)	LOPE (n=11)	Healthy pregnancy (n=18)
Age (y)	34.0 ± 7.1	33.0 ± 4.0	31.1 ± 3.8
BMI (kg/m²)	27.6 ± 4.8	28.3 ± 3.4	24.5 ± 5.5
GA at delivery (wk)	31.3 ± 2.5 ^¶&	37.0 ± 1.9 ^§&	39.4 ± 0.9 ^¶§
Number of pregnancies (no.)	1.6 ± 0.9	2 ± 0.9	1.7 ± 0.8
Systolic blood pressure (mm Hg)	156.4 ± 26.5 ^¶	144.4 ± 16.8 ^§	113.9 ± 8.5 ^¶§
Diastolic blood pressure (mm Hg)	103.1 ± 10.7 ^¶	96.9 ± 11.6 ^§	72.2 ± 8.1 ^¶§
Heart rate (bpm)	94.6 ± 29.8 ^¶	82.8 ± 16.5	74.4 ± 6.3 ^¶
Medications
Methyldopa (no. [%])	17 [100%]	11 [100%]	0 [0%]
Amlodipine (no. [%])	5 [29.4%]	2 [18.2%]	0 [0%]
Dexamethasone (no. [%])	11 [64.7%]	1 [9.1%]	0 [0%]
Nadroparin calcium (no. [%])	3 [17.6%]	1 [9.1%]	0 [0%]
Diazepam (no. [%])	3 [17.6%]	8 [72.7%]	0 [0%]
Magnesium sulphate (no. [%])	4 [23.5%]	1 [9.1%]	0 [0%]
Nifedipine (no. [%])	0 [0%]	1 [9.1%]	0 [0%]
Low-sodium diet (no. [%])	11 [64.7%]	8 [72.7%]	0 [0%]

BMI, body mass index; bpm, beats per minute; EOPE, early-onset pre-eclampsia; GA, gestational age; LOPE, late-onset pre-eclampsia.

¶, P <0.05 of a characteristic between EOPE and healthy pregnancy groups.

§, P <0.05 of a characteristic between LOPE and healthy pregnancy groups.

&, P <0.05 of a characteristic between EOPE and LOPE.

No competing interests reported.

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Maternal plasma proteome profiling of biomarkers and pathogenic mechanisms of early-onset and late-onset preeclampsia

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