Proteomics Analysis of Serum Proteins in Gestational Diabetes

Jianhua Li The First Afliated Hospital of Fujian Medical University Lin Lu The First Afliated Hospital of Fujian Medical University Xinping Xie The First Afliated Hospital of Fujian Medical University Xiaofeng Dai The First Afliated Hospital of Fujian Medical University Lihong Chen (  [email protected] ) First Afliated Hospital of Fujian Medical University https://orcid.org/0000-0001-7552-3533 Shan Zheng The First Afliated Hospital of Fujian Medical University

Research Article

Keywords: Adipokines, Coagulation, Gestational diabetes mellitus, Proteomics

Posted Date: June 3rd, 2021

DOI: https://doi.org/10.21203/rs.3.rs-313262/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License

Page 1/14 Abstract Purpose

The purpose of this study was to screen serum proteins for biomarkers of gestational diabetes mellitus (GDM) and to investigate its pathogenesis by analyzing the differences in serum proteomics between pregnant women with GDM and healthy pregnant women.

Methods

High performance liquid chromatography-mass spectrometry was used to identify differentially expressed serum proteins between pregnant women with GDM and healthy pregnant women, and bioinformatics analysis was then performed on the identifed proteins.

Results

A total of 1152 quantifable proteins were detected; among them, 15 were up-regulated in the serum of GDM pregnant women, while 26 were down-regulated. The subsequent parallel reaction monitoring (PRM) assay validated the expression levels of 12 out of 41 differentially expressed proteins. Moreover, bioinformatics analysis revealed that the differentially expressed proteins are involved in multiple biological processes and signaling pathways related to lipid metabolism, glycan degradation, immune response, and platelet aggregation.

Conclusions

This study identifed 41 serum proteins with differential expression between pregnant women with GDM and healthy pregnant women, providing new candidate molecules for elucidating GDM pathogenesis and screening therapeutic targets.

Introduction

Gestational diabetes mellitus (GDM) is defned as a varying degree of glucose tolerance disorder that occurs or is frst recognized during pregnancy [1]. As the most common complication of pregnancy, GDM seriously threatens the short-term and long-term health of mothers and their offspring, becoming a growing public health problem worldwide [2]. Although GDM has been shown to be related to adipokines [3], infammation [4], insulin resistance, and cellular dysfunction [5], its pathogenesis has not yet to be fully elucidated. At present, the clinical diagnosis of GDM is conducted mainly based on the analysis of hyperglycemia and the study of adverse pregnancy outcomes using criteria issued by the International Association of Diabetes and Pregnancy Study Groups. However, this diagnosis method has certain defects, causing a misdiagnosis of mild glucose intolerance as GDM [6]. Thus, pathogenesis studies and identifcation of new diagnostic markers could make GDM diagnosis more accurate and help to control the occurrence and development of this disease.

Page 2/14 Proteomics, which has been widely used in basic research and clinical diagnosis of human diseases, involves analysis, characterization and classifcation of all protein in a genome[7]. In particular, differential proteomics provides a powerful technique for identifying different molecules based on comparison of the changes in protein levels between samples, investigating disease-related differential proteomics as a whole, and searching for protein markers of diseases. With the help of differential proteomics, the pathogenesis of GDM can be further explored due to the fact that the protein secreted into the maternal circulation refects the physiological or disease state, and change in the expression level indicates the risk [8]. So far, comparative analysis of proteomics from different sources in different periods of GDM has been conducted in a number of studies [9, 10], showing a great potential of proteomics in GDM research.

In this study, mass spectrometry-based differential proteomics analysis was performed to screen differentially expressed proteins in serum samples between normal pregnancy and pregnancy with GDM. Bioinformatics analysis revealed that most differential regulatory proteins in GDM are related to coagulation function, lipid metabolism, immune response and infammation. These proteins can be used as GDM biomarkers and facilitate the early diagnosis and prognosis of the disease. Meanwhile, targeting GDM-related factors could provide new strategies for the clinical treatment of GDM.

Materials And Methods

Serum samples

The patients who were admitted to the First Afliated Hospital of Fujian Medical University from June 2019 to January 2020 were included in the study. According to the medical history and results of 75 g oral glucose tolerance test (OGTT), these patients were divided into normal pregnant women group (n=8) and GDM pregnant women group (n=12). After written informed consents were obtained, blood samples were collected. 5 mL blood samples were taken and placed in a glass tube without any additives. The blood was clotted for 30 min at room temperature and centrifuged at 3000g for 10 min at 4 ℃, and the separated serum was stored at -80 ℃. The study was approved by the hospital ethical committee.

Protein extraction

An appropriate amount of SDT lysis buffer (4% sodium dodecyl sulfate, 0.1 M dithiothreitol, 100 mM Tris-HCl) was added to the serum, mixed well, and then bathed in boiling water for 10 min. After centrifugation at 14000 g for 15 min, the supernatant was taken and then fltered using a 0.22 µm centrifuge tube to collect the fltrate. The BCA method was used for protein quantifcation. The protein samples were divided and stored at -80 ℃. The protein extraction was performed on serum samples of 12 pregnant women with GDM and 8 normal pregnant women.

SDS-PAGE electrophoresis

The quality of protein samples was evaluated by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). For each sample, 20 μg of protein was dissolved in SDS-PAGE loading buffer (6X) and boiled for 5 min. The protein was loaded on a 12% SDS-PAGE gel, stained with Coomassie Brilliant Blue, and then electrophoresed (constant voltage 250 V, 40 min).

Page 3/14 FASP enzymatic hydrolysis

200 ug of protein solution was taken from each sample and mixed with dithiothreitol at a fnal concentration of 100 mM. The mixture was incubated in boiling water bath for 5 min and then allowed to cool to room temperature. 100 mM iodoacetamide buffer was added to the mixture, oscillated and reacted at room temperature in dark for 30 min prior to centrifugation. Subsequently, 4 g Trypsin was added to the fltrate, shaken for 1min, and centrifuged at 37 ℃ for 16-18 h. After being collected, the peptides were desalted using C18 Cartridge, lyophilized, reconstituted with 40 μL of 0.1% formic acid solution, and subjected to a quantifcation.

HPLC Fractionation

The tryptic peptides were fractionated by high pH reverse-phase HPLC using XBridge Peptide BEH C18 Column (Waters, 130Å, 5 µm, 4.6 mm × 100 mm). Briefy, peptides were frst separated into 48 fractions with a gradient of 5% to 45% acetonitrile (pH 10.0) over 40 min, and then combined into 12 fractions and dried by vacuum centrifuging.

LC-MS/MS Analysis

The tryptic peptides were dissolved in 0.1% formic acid (solvent A) and then directly loaded onto the Acclaim PepMap RSLC C18 column (Scientifc Thermo Fisher, 50um × 15cm). The gradient was comprised of holding at 1% for the frst 5 min, an increase from 1% to 28% solvent B (0.1% formic acid in 80% acetonitrile) over 90 min, 28% to 38% for 15 min, and climbing to 100% in 5 min, at a constant fow rate of 300 nL/min on an Easy nLC system (Scientifc Thermo Fisher).

The peptides were analyzed by the electrospray ionization tandem mass spectrometry (ESI-MS/MS) in Q- Exactive HF-X (Scientifc Thermo Fisher) coupled online to the Easy nLC. The electrospray voltage of 2.0 kV was applied. The m/z scan range of primary MS and scanning resolution were set as 350 to 1500 and 60,000, respectively. Peptides were then selected for MS analysis using NCE setting of 28, and the fragments were detected at a resolution of 15,000. Automatic gain control (AGC) was set at 2E5, while the maximum injection time (IT) and dynamic exclusion duration were set to 45 ms and 30.0 s, respectively.

The original mass spectrometry data were merged and analyzed by Spectronaut Pulsar X (version 12, Biognosys AG) to establish a spectral database, Uniprot_HomoSapiens_20386_20180905 (http://www.uniprot.org). For the database search, trypsin digestion was set, with two missed cutting sites allowed. Carbamidomethylation on cysteine (C) was defned as a fxed modifcation of library search parameters, while variable modifcations included oxidation on methionine (M) and acetylation on the protein N-terminus. Both precursor and peptide FDR were controlled at 1%. For identifcation of differentially expressed proteins, the fold changes and p-value (Student's t test) should be >1.2 or <0.83 and <0.05, respectively.

PRM assays

The parallel reaction monitoring (PRM) method was used to verify the differentially expressed proteins. Peptide separation was performed on the Acclaim PepMap RSLC C18 analytical column using a multistep gradient of B: 2% to 8% for 1 min, 8% to 28% for 10 min, 28% to 40% for 10 min and 40% to 90% for 1 min. Then B was held

Page 4/14 at 90% for 3 min. A fow rate of 300 nL/min was used and the analysis duration was 60 min. ESI source settings were the same as above. The PRM analysis included a full MS1 event followed by up to 10 targeted MS2 events. The m/z scan range of MS1 and scanning resolution were set as 350 to 1500 and 60,000, respectively. AGC target value was set to 3e6 and maximum IT was 45 ms. For MS2, scan settings were 15,000 resolving power, AGC target 2e5, maximum IT 45 ms, normalized collision energy 27 eV and isolation window was set to 2.0 m/z.

Bioinformatics analysis

NCBI BLAST+ (ncbi-blast-2.3.0+) was utilized to compare the gene ontology (GO) annotations of differentially expressed proteins with the appropriate protein sequence database, and the top 10 aligned sequences that meet the appropriate E-value<=1e-3 were retained for subsequent analysis. Functional annotation was carried out using the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://geneontology.org/). Both GO and KEGG pathway enrichment analyses were performed based on Fisher's exact test. In clustering analysis, quantitative data of the target protein set were normalized frst. Then, the software matplotlib was applied to classify samples and protein expression in two dimensions at the same time (Euclidean distance, average linkage clustering). The hierarchical cluster membership was defned by plotting a heat map.

Results

Protein identifcation

Quantitative and SDS-PAGE analyses showed that the protein was of good quality, sufcient and good in parallelism between samples (Figure S1). Moreover, quantitative normalization was carried out for each group of samples to ensure the accuracy of mass spectrometry results (Figure S2).

A total of 8135 specifc peptides and 1152 proteins were identifed in all samples. The comparison between the pregnant women with GDM and controls led to an identifcation of 41 differentially expressed proteins based on the protein identifcation criteria; among them, 15 were up-regulated, while 26 were down-regulated in the GDM group (Figure 1 and Table 1).

PRM verifcation

To verify the expression levels of differentially expressed proteins, PRM was employed to analyze the expression patterns of 41 target proteins in the same sample. In the experiments, only 12 target proteins were quantifed due to restrictions in the characteristics and expression abundance of certain proteins (Table 2). Strikingly, PRM-based verifcation obtained consistent results with those of the proteomics analysis.

Bioinformatics analysis

GO annotations were performed to identify biological processes, molecular functions and cellular components related to the differentially expressed proteins. As shown in Figure 2A, the main biological processes involve cellular process, metabolic process, biological regulation, response to stimulus and immune system process, while the main involved molecular functions and cell components include binding and catalytic activities and extracellular region part, cell part, protein-containing complex, and membrane, respectively. Moreover, GO

Page 5/14 analysis revealed that the differentially expressed proteins are enriched in either biological processes such as metabolism of lipoprotein, fucose, glycoside, and cholesterol, or molecular functions including activities of alpha-L-fucosidase and arylesterase, and binding of cholesterol and phospholipid, or cell compositions such as high-density lipoprotein particle and cytoplasmic vesicle membrane (Figure 2B). Meanwhile, the KEGG pathway annotation showed that the differentially expressed proteins are enriched in cholesterol metabolism, other glycan degradation and lysosome (Figure 2C). As shown in Figure 4, hierarchical cluster analysis of the two experimental groups demonstrated that GDM pathogenesis involves dysfunction of numerous pathways.

Discussion

Although advanced age, overweight and obesity have been shown to be risk factors for GDM [11], its pathogenesis remains elusive. To elucidate the molecular mechanism of GDM and to search for potential serum markers for early diagnosis, we identifed and quantitatively analyzed the differentially expressed proteins in the serum of pregnant women with gestational diabetes and healthy controls. A total of 41 differentially expressed proteins were identifed between the two groups. We further showed that these proteins are correlated with distinct biological processes and signal transduction pathways, suggesting that development of GDM may involve various mechanisms and proteins, including blood coagulation, fbrin clot formation, lipid metabolism, immune and infammatory responses.

Under normal physiological conditions, pregnancy leads to activation of the coagulation system and fbrinolysis system, promoting the balance of coagulation function and ensuring the stability of the body's coagulation. Compared with normal pregnant women, pregnant women with GDM exhibit more obvious changes in coagulation and fbrinolysis. The occurrence of diabetes enhances the activation of platelets and coagulation factors [12]. It was reported that the levels of prothrombin time and activated partial thrombin time of GDM patients were signifcantly lower than those of the non-pregnant group and healthy pregnancy group, whereas fbrinogen and plasminogen levels in GDM patients were markedly increased [13]. Here, we found that alpha-2-macroglobulin (A2MG) and fbrinogen alpha chain (FIBA) were down-regulated in the GDM group. A2MG is a broad-spectrum glycosylation inhibitor of various proteases including blood coagulation-related proteases, which is believed to be associated with infammatory response and coagulant properties [14, 15]. FIBA is chopped up by the protease thrombin to generate monomers that are subsequently polymerized with fbrinogen beta and fbrinogen gamma to form an insoluble fbrin matrix [16]. As one of the main components of blood clots, fbrin plays a major role in hemostasis and thrombosis [17], making pregnant women with GDM more prone to hypercoagulable state. Furthermore, we observed that in the GDM group, coagulation factors X and XII were up-regulated, whereas coagulation factors including V, XIII, and others were down-regulated, albeit no signifcant difference between the GDM group and controls was present. Collectively, these results suggest that the apparent hypercoagulability of pregnant women is a potential risk factor for GDM patients.

Poor blood glucose control in diabetic pregnant women affects not only the blood coagulation system, but also lipid metabolism [18]. Studies have shown that in patients with poorly controlled GDM, higher concentrations of high-density lipoproteins and higher triglyceride levels were detected in the third trimester, and the second and third trimesters, respectively. Another study suggested that APOA-II, APOC3 [19], APOE and PON1 [9] might be candidate biomarkers for GDM. Consistent with the above studies, we observed signifcantly higher levels of apolipoproteins APOE and APOC3 in the serum of GDM pregnant women. Moreover, we showed that the level of

Page 6/14 apolipoprotein APOL1 increased signifcantly, whereas the levels of cholesteryl ester transfer protein (CETP) and zinc-alpha-2-glycoprotein (ZA2G) decreased markedly. CETP regulates the reverse transport of cholesterol, by which excess cholesterol is removed from surrounding tissues and sent back to the liver for elimination. ZA2G is a novel adipokine related to lipid metabolism that stimulates lipid degradation in adipocytes and causes extensive fat losses associated with certain advanced cancers [20]. Mracek et al. [21] suggested that ZA2G has a protective effect on the development of obesity and is related to insulin resistance. Together, these results support the role of lipid metabolism and transport in GDM and suggest its potential for prediction and diagnosis of GDM

In this study, we identifed ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2) as the most up-regulated protein in the GDM group, which is implicated in obesity-related metabolic phenotypes. It has been reported that increased ENPP2 expression in adipose tissue is closely related to impaired glucose homeostasis and/or insulin resistance [22], while ENPP2 gene regulation affects immune cell population and tissue infammation in adipose tissue [23]. Although studies have shown that ENPP2 may play a role in induction of parturition [24, 25], the relationship between ENPP2 and the onset of GDM remains to be determined. Given that increased expression of ENPP2 protein may be related to GDM, it needs to be further investigated whether ENPP2 is involved in GDM pathogenesis and can act as a potential biomarker.

In the present study, we performed proteomic analysis on serum samples of the pregnant women to identify the differentially expressed proteins related to GDM. A total of 41 proteins displayed an expression pattern that was signifcantly different between pregnant women with GDM and normal pregnant women. Furthermore, bioinformatics analysis suggested that the occurrence and development of GDM may involve multiple biological and pathological processes, such as blood coagulation, fbrin clot formation, lipid metabolism, immunity and infammation. Thus, proteins related to the above processes could have potentials as candidate biomarkers for predicting GDM or as intervention targets for preventing the development of GDM.

Declarations

Funding: This study is supported by the Guiding Project of Fujian Science and Technology Department (2017Y0034).

Competing interests: The authors have declared no confict of interest.

Ethics approval: The study was approved by the ethical committee of the First Afliated Hospital of Fujian Medical University.

Consent to participate: The consent to participate of this study have been approved.

Consent for publication: Written consent to publish this information was obtained from study participants.

Availability of data and material: The analyzed data can be obtained for non-commercial use by contacting the corresponding author.

Code availability: Not applicable.

Page 7/14 Authors' contributions: Jianhua Li: Project development, Manuscript writing; Lin Lu: Manuscript writing; Xinping Xie: Data analysis; Xiaofeng Dai: Data collection; Lihong Chen: Project development; Shan Zheng: Manuscript revising.

References

1. Chen Q, Yang H, Feng Y, Zhang P, Wu W, Li S, Thompson B, Wang X, Peng T, Wang F, Xie B, Guo P, Li M, Wang Y, Zhao N, Wang S, Zhang Y (2018) SOS1 gene polymorphisms are associated with gestational diabetes mellitus in a Chinese population: Results from a nested case-control study in Taiyuan, China. Diab Vasc Dis Res 15(2):158–161. doi:10.1177/1479164117745260 2. Chen L, Mayo R, Chatry A, Hu G (2016) Gestational Diabetes Mellitus: Its Epidemiology and Implication beyond Pregnancy. Current Epidemiology Reports 3(1):1–11 3. Shang M, Dong X, Hou L (2018) Correlation of adipokines and markers of oxidative stress in women with gestational diabetes mellitus and their newborns. J Obstet Gynaecol Res 44(4):637–646. doi:10.1111/jog.13586 4. Bossick AS, Peters RM, Burmeister C, Kakumanu N, Shill JE, Cassidy-Bushrow AE (2016) Antenatal infammation and gestational diabetes mellitus risk among pregnant African-American women. J Reprod Immunol 115:1–5. doi:10.1016/j.jri.2016.03.005 5. de Gennaro G, Palla G, Battini L, Simoncini T, Del Prato S, Bertolotto A, Bianchi C (2019) The role of adipokines in the pathogenesis of gestational diabetes mellitus. Gynecol Endocrinol 35(9):737–751. doi:10.1080/09513590.2019.1597346 6. K CK, Zhang H, Vaidya A (2019) Increased Incidence in False Positive Diagnosis of Gestational Diabetes Mellitus with 75 gm Oral Glucose Tolerance Test: A Clinical Study in Chinese Women. J Nepal Health Res Counc 17(1):103–108. doi:10.33314/jnhrc.1588 7. Cifani P, Kentsis A (2017) Towards comprehensive and quantitative proteomics for diagnosis and therapy of human disease. Proteomics 17 (1–2). doi:10.1002/pmic.201600079 8. Mavreli D, Evangelinakis N, Papantoniou N, Kolialexi A (2020) Quantitative Comparative Proteomics Reveals Candidate Biomarkers for the Early Prediction of Gestational Diabetes Mellitus: A Preliminary Study. In Vivo 34(2):517–525. doi:10.21873/invivo.11803 9. Shen L, Zhao D, Chen Y, Zhang K, Chen X, Lin J, Li C, Iqbal J, Zhao Y, Liang Y, Wei Y, Feng C (2019) Comparative Proteomics Analysis of Serum Proteins in Gestational Diabetes during Early and Middle Stages of Pregnancy. Proteomics Clin Appl 13(5):e1800060. doi:10.1002/prca.201800060 10. Liao Y, Xu GF, Jiang Y, Zhu H, Sun LJ, Peng R, Luo Q (2018) Comparative proteomic analysis of maternal peripheral plasma and umbilical venous plasma from normal and gestational diabetes mellitus pregnancies. Med (Baltim) 97(36):e12232. doi:10.1097/MD.0000000000012232 11. Yen IW, Lee CN, Lin MW, Fan KC, Wei JN, Chen KY, Chen SC, Tai YY, Kuo CH, Lin CH, Hsu CY, Chuang LM, Lin SY, Li HY (2019) Overweight and obesity are associated with clustering of metabolic risk factors in early pregnancy and the risk of GDM. PLoS One 14(12):e0225978. doi:10.1371/journal.pone.0225978 12. Grandl G, Wolfrum C (2018) Hemostasis, endothelial stress, infammation, and the metabolic syndrome. Semin Immunopathol 40(2):215–224. doi:10.1007/s00281-017-0666-5

Page 8/14 13. Gorar S, Alioglu B, Ademoglu E, Uyar S, Bekdemir H, Candan Z, Saglam B, Koc G, Culha C, Aral Y (2016) Is There a Tendency for Thrombosis in Gestational Diabetes Mellitus? J Lab Physicians 8(2):101–105. doi:10.4103/0974-2727.180790 14. Calvert L, Atkinson H, Berry L, Chan A (2019) Age-Dependent Variation in Glycosylation Features of Alpha-2- Macroglobulin. Cell Biochem Biophys 77(4):335–342. doi:10.1007/s12013-019-00883-4 15. Shimomura R, Nezu T, Hosomi N, Aoki S, Sugimoto T, Kinoshita N, Araki M, Takahashi T, Maruyama H, Matsumoto M (2018) Alpha-2-macroglobulin as a Promising Biological Marker of Endothelial Function. J Atheroscler Thromb 25(4):350–358. doi:10.5551/jat.41335 16. Mosesson MW (2005) Fibrinogen and fbrin structure and functions. J Thromb Haemost 3(8):1894–1904. doi:10.1111/j.1538-7836.2005.01365.x 17. Litvinov RI, Weisel JW (2017) Fibrin mechanical properties and their structural origins. Matrix Biol 60– 61:110–123. doi:10.1016/j.matbio.2016.08.003 18. Teliga-Czajkowska J, Sienko J, Zareba-Szczudlik J, Malinowska-Polubiec A, Romejko-Wolniewicz E, Czajkowski K (2019) Infuence of Glycemic Control on Coagulation and Lipid Metabolism in Pregnancies Complicated by Pregestational and Gestational Diabetes Mellitus. Adv Exp Med Biol 1176:81–88. doi:10.1007/5584_2019_382 19. Flood-Nichols SK, Lutgendorf MA, Mesngon MT, Harroun AJ, Cesarini MS, Napolitano PG, Ippolito DL (2013) Impaired lipid transport in gestational diabetes mellitus. Journal of Diabetes Research Clinical Metabolism 22(1):22 20. Kabil SL, Mahmoud NM (2018) Canaglifozin protects against non-alcoholic steatohepatitis in type-2 diabetic rats through zinc alpha-2 glycoprotein up-regulation. Eur J Pharmacol 828:135–145. doi:10.1016/j.ejphar.2018.03.043 21. Bing C, Mracek T, Gao D, Trayhurn P (2010) Zinc-alpha2-glycoprotein: an adipokine modulator of body fat mass? Int J Obes (Lond) 34(11):1559–1565. doi:10.1038/ijo.2010.105 22. Reeves VL, Trybula JS, Wills RC, Goodpaster BH, Dube JJ, Kienesberger PC, Kershaw EE (2015) Serum Autotaxin/ENPP2 correlates with insulin resistance in older humans with obesity. Obesity (Silver Spring) 23(12):2371–2376. doi:10.1002/oby.21232 23. Nishimura S, Nagasaki M, Okudaira S, Aoki J, Ohmori T, Ohkawa R, Nakamura K, Igarashi K, Yamashita H, Eto K, Uno K, Hayashi N, Kadowaki T, Komuro I, Yatomi Y, Nagai R (2014) ENPP2 contributes to adipose tissue expansion and insulin resistance in diet-induced obesity. Diabetes 63(12):4154–4164. doi:10.2337/db13-1694 24. Tokumura A, Majima E, Kariya Y, Tominaga K, Kogure K, Yasuda K, Fukuzawa K (2002) Identifcation of human plasma lysophospholipase D, a lysophosphatidic acid-producing enzyme, as autotaxin, a multifunctional phosphodiesterase. J Biol Chem 277(42):39436–39442. doi:10.1074/jbc.M205623200 25. Waite C, Mejia R, Ascoli M (2016) Gq/11-Dependent Changes in the Murine Ovarian Transcriptome at the End of Gestation. Biol Reprod 94(3):62. doi:10.1095/biolreprod.115.136952

Tables

Table 1. The differentially expressed proteins identified in the different groups.

Page 9/14 Protein Names Accessions Gene Names Fold P value Change

ENPP2 Q13822 ENPP2 2.41009 0.00099 LCAP Q9UIQ6 LNPEP 2.12435 0.02582

PSG5 Q15238 PSG5 1.85227 0.03598

ADA12 O43184 ADAM12 1.82532 0.02463

UBB;UBC;RS27A;RL40 P0CG47;P0CG48;P62979;P62987 UBB;UBC;RPS27A;UBA52 1.77024 0.01534

HV70D A0A0C4DH43 IGHV2-70D 1.74216 0.01042

PSG11 Q9UQ72 PSG11 1.60007 0.00165

FIBA P02671 FGA 1.52207 0.00363

CYTC P01034 CST3 1.48321 0.01388

APOE P02649 APOE 1.44068 0.03843

APOC3 P02656 APOC3 1.38564 0.00234

FUCO2 Q9BTY2 FUCA2 1.33303 0.01546

GNPTG Q9UJJ9 GNPTG 1.32306 0.00226

FUCO P04066 FUCA1 1.31738 0.03303

APOL1 O14791 APOL1 1.29031 0.00612

PON1 P27169 PON1 0.82963 0.02868

IGLC2 P0DOY2 IGLC2 0.82383 0.03519

IGLC3 P0DOY3 IGLC3 0.82383 0.03519

CR063 Q68DL7 C18orf63 0.81965 0.02107

GELS P06396 GSN 0.81486 0.00262

ZA2G P25311 AZGP1 0.80875 0.01114

DDX55 Q8NHQ9 DDX55 0.78727 0.02054

IC1 P05155 SERPING1 0.78424 0.02103

APOA4 P06727 APOA4 0.78039 0.00947

KV315 P01624 IGKV3-15 0.76482 0.02777

LV743 P04211 IGLV7-43 0.73672 0.04679

ECM1 Q16610 ECM1 0.73321 0.01806

A2MG P01023 A2M 0.71869 0.00313

PGRP2 Q96PD5 PGLYRP2 0.69928 0.02926

APMAP Q9HDC9 APMAP 0.69828 0.02107

SAA4 P35542 SAA4 0.69042 0.03903

KIF5C O60282 KIF5C 0.66998 0.01965

KINH P33176 KIF5B 0.66998 0.01965

KIF5A Q12840 KIF5A 0.66998 0.01965

Page 10/14 FHR5 Q9BXR6 CFHR5 0.66876 0.03012

CD44 P16070 CD44 0.66069 0.00586

LV301 P01715 IGLV3-1 0.64062 0.03926

LV39 A0A075B6K5 IGLV3-9 0.55739 0.02912

CETP P11597 CETP 0.55685 0.00143

HV169 P01742 IGHV1-69 0.55229 0.02411

FA8 P00451 F8 0.47465 0.02275

Table 2 PRM quantification results

Protein Name Accessions Gene Name Quantification Type PRM P value

ENPP2 Q13822 ENPP2 MS2 2.4777 0.0007 LCAP Q9UIQ6 LNPEP MS2 2.1963 0.0232 ADA12 O43184 ADAM12 MS2 1.8293 0.0307 FIBA P02671 FGA MS2 1.5337 0.0089 CYTC P01034 CST3 MS2 1.3124 0.0849 APOE P02649 APOE MS1+MS2 1.4429 0.0438 APOC3 P02656 APOC3 MS2 1.6852 0.0013 APOL1 O14791 APOL1 MS2 1.1492 0.4204 ZA2G P25311 AZGP1 MS2 0.9674 0.7255

A2MG P01023 A2M MS1+MS2 0.7891 0.0749 SAA4 P35542 SAA4 MS2 0.6903 0.0770 CETP P11597 CETP MS2 0.7305 0.1371

Figures

Page 11/14 Figure 1

Volcano plot of differentially abundant proteins (fold change ≥ 1.2 or fold change ≤ 0.83 and p-value < 0.05) in the samples between pregnant women with GDM and normal pregnant women.

Page 12/14 Figure 2

(A) Level 2 statistics of GO function annotations of differentially expressed proteins. (B) GO enrichment analysis of differentially expressed proteins (top 10). (C) KEGG pathway enrichment analysis of differentially expressed proteins (top 10).

Page 13/14 Figure 3

Hierarchical cluster analysis of differentially expressed proteins between the two different groups, including A (GDM pregnant women) and B (normal pregnant women).

Supplementary Files

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