Research ArticleProteomics Analysis of Serum Proteins in Gestational Diabetes

Jianhua Li, Lin Lu, Xinping Xie, Xiaofeng Dai, Shan Zheng, and Lihong Chen

Department of Obstetrics and Gynecology, �e First Affiliated Hospital of Fujian Medical University, Fuzhou,Fujian 350005, China

Correspondence should be addressed to Lihong Chen; clhljh1005@126.com

Received 2 September 2021; Accepted 6 October 2021; Published 2 November 2021

Academic Editor: Songwen Tan

Copyright © 2021 Jianhua Li et al. )is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. )e purpose of this study was to screen serum proteins for biomarkers of gestational diabetes mellitus (GDM) and toinvestigate its pathogenesis by analyzing the differences in serum proteomics between pregnant women with GDM and healthypregnant women. Methods. Patients who were admitted to the First Affiliated Hospital of Fujian Medical University from June2019 to January 2020 were included. According to the medical history and the results of the 75 g oral glucose tolerance test(OGTT), they were divided into the normal pregnant women group and GDM pregnant women group. )e serum of two groupsof patients was collected. High performance liquid chromatography-mass spectrometry was used to identify differentiallyexpressed serum proteins between pregnant women with GDM and healthy pregnant women, and bioinformatics analysis wasthen performed on the identified proteins. Results. A total of 1152 quantifiable proteins were detected; among them, 15 wereupregulated in serum of GDM pregnant women, while 26 were downregulated. )e subsequent parallel reaction monitoring(PRM) assay validated the expression levels of 12 out of 41 differentially expressed proteins. Moreover, bioinformatics analysisrevealed that the differentially expressed proteins are involved in multiple biological processes and signaling pathways related tothe lipid metabolism, glycan degradation, immune response, and platelet aggregation. Conclusions. )is study identified 41 serumproteins with differential expression between pregnant women with GDM and healthy pregnant women, providing new candidatemolecules for elucidating GDM pathogenesis and screening therapeutic targets.

1. Introduction

Gestational diabetes mellitus (GDM) is defined as a varyingdegree of glucose tolerance disorder that occurs or is firstrecognized during pregnancy [1]. As the most commoncomplication of pregnancy, GDM seriously threatens theshort-term and long-term health of mothers and their off-spring, becoming a growing public health problem world-wide [2]. Although GDM has been shown to be related toadipokines [3], inflammation [4], insulin resistance, andcellular dysfunction [5], its pathogenesis has not yet to befully elucidated. At present, the clinical diagnosis of GDM isconducted mainly based on the analysis of hyperglycemiaand the study of adverse pregnancy outcomes using criteriaissued by the International Association of Diabetes andPregnancy Study Groups. However, this diagnosis methodhas certain defects, causing a misdiagnosis of mild glucoseintolerance as GDM [6]. )us, pathogenesis studies andidentification of new diagnostic markers could make GDM

diagnosis more accurate and help to control the occurrenceand development of this disease.

Proteomics, which has been widely used in basic re-search and clinical diagnosis of human diseases, involvesanalysis, characterization, and classification of all proteinin a genome [7]. In particular, differential proteomicsprovides a powerful technique for identifying differentmolecules based on comparison of the changes in proteinlevels between samples, investigating disease-related dif-ferential proteomics as a whole, and searching for proteinmarkers of diseases. With the help of differential pro-teomics, the pathogenesis of GDM can be further exploreddue to the fact that the protein secreted into the maternalcirculation reflects the physiological or disease state, andchange in the expression level indicates the risk [8]. So far,comparative analysis of proteomics from different sourcesin different periods of GDM has been conducted in anumber of studies [9, 10], showing a great potential ofproteomics in GDM research.

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2021, Article ID 4724590, 8 pageshttps://doi.org/10.1155/2021/4724590

In this study, mass spectrometry-based differentialproteomics analysis was performed to screen differentiallyexpressed proteins in serum samples between normalpregnancy and pregnancy with GDM. Bioinformaticsanalysis revealed that most differential regulatory proteins inGDM are related to coagulation function, lipid metabolism,immune response, and inflammation. )ese proteins can beused as GDM biomarkers and facilitate the early diagnosisand prognosis of the disease. Meanwhile, targeting GDM-related factors could provide new strategies for the clinicaltreatment of GDM.

2. Materials and Methods

2.1. Serum Samples. )e patients who were admitted to theFirst Affiliated Hospital of Fujian Medical University fromJune 2019 to January 2020 were included in the study.According to the medical history and results of the 75 g oralglucose tolerance test (OGTT), that is, the patient can take75 g of glucose solution orally, compare the blood glucoselevel with the fasting blood glucose level after two hours. Ifthe fasting blood glucose is greater than 7.0mmol/L, theblood glucose over 11.1mmol/L two hours after the oralglucose tolerance test can be considered as diabetes. )esepatients were divided into the normal pregnant womengroup (n� 8) and GDM pregnant women group (n� 12).After written informed consents were obtained, bloodsamples were collected. 5mL blood samples was taken andplaced in a glass tube without any additives. )e blood wasclotted for 30min at room temperature and centrifuged at3000 g for 10min at 4°C, and the separated serum was storedat −80°C. )e study was approved by the hospital ethicalcommittee.

2.2. Protein Extraction. An appropriate amount of SDT lysisbuffer (4% sodium dodecyl sulfate, 0.1M dithiothreitol, and100mMTris-HCl) was added to serum, mixed well, and thenbathed in boiling water for 10min. After centrifugation at14000 g for 15min, the supernatant was taken and thenfiltered using a 0.22 µm centrifuge tube to collect the filtrate.)e BCA method was used for protein quantification. )eprotein samples were divided and stored at −80°C. )eprotein extraction was performed on serum samples of 12pregnant women with GDM and 8 normal pregnant women.

2.3. SDS-PAGE Electrophoresis. )e quality of proteinsamples was evaluated by sodium dodecyl sulfate poly-acrylamide gel electrophoresis (SDS-PAGE). For eachsample, 20 μg of protein was dissolved in SDS-PAGE loadingbuffer (6X) and boiled for 5min.)e protein was loaded on a12% SDS-PAGE gel, stained with Coomassie Brilliant Blue,and then electrophoresed (constant voltage 250V, 40min).

2.4. FASP Enzymatic Hydrolysis. 200 µg of protein solutionwas taken from each sample andmixed with dithiothreitol ata final concentration of 100mM.)e mixture was incubatedin a boiling waterbath for 5min and then allowed to cool to

room temperature. 100mM iodoacetamide buffer was addedto the mixture, oscillated, and reacted at room temperaturein dark for 30min prior to centrifugation. Subsequently, 4 gtrypsin was added to the filtrate, shaken for 1min, andcentrifuged at 37°C for 16–18 h. After being collected, thepeptides were desalted using C18 Cartridge, lyophilized,reconstituted with 40 μL of 0.1% formic acid solution, andsubjected to quantification.

2.5. HPLC Fractionation. )e tryptic peptides (ShanghaiYaxin Biological Co., Ltd.) were fractionated by high pHreverse-phase HPLC using the XBridge Peptide BEH C18column (Waters, 130 A, 5 µm, 4.6mm× 100mm). Briefly,peptides were first separated into 48 fractions with a gradientof 5–45% acetonitrile (pH 10.0) over 40min and thencombined into 12 fractions and dried by vacuumcentrifuging.

2.6. LC-MS/MS Analysis. )e tryptic peptides were dis-solved in 0.1% formic acid (solvent A) and then directlyloaded onto the Acclaim PepMap RSLC C18 column (Sci-entific )ermo Fisher, 50 µm× 15 cm). )e gradient wascomprised of holding at 1% for the first 5min, an increasefrom 1% to 28% solvent B (0.1% formic acid in 80% ace-tonitrile) over 90min, 28% to 38% for 15min, and climbingto 100% in 5min, at a constant flow rate of 300 nL/min on anEasy nLC system (Scientific )ermo Fisher).

)e peptides were analyzed by the electrospray ioniza-tion tandem mass spectrometry (ESI-MS/MS) in Q ExactiveHF-X (Scientific )ermo Fisher) coupled online to the EasynLC.)e electrospray voltage of 2.0 kV was applied.)em/zscan range of primary MS and scanning resolution were setas 350–1500 and 60,000, respectively. Peptides were thenselected for MS analysis using NCE setting of 28, and thefragments were detected at a resolution of 15,000. Automaticgain control (AGC) was set at 2e5, while the maximuminjection time (IT) and dynamic exclusion duration were setto 45ms and 30.0 s, respectively.

)e original mass spectrometry data were merged andanalyzed by Spectronaut Pulsar X (version 12, BiognosysAG) to establish a spectral database, Uni-prot_HomoSapiens_20386_20180905 (http://www.uniprot.org). For the database search, trypsin digestion was set,with two missed cutting sites allowed. Carbamidomethy-lation on cysteine (C), was defined as a fixed modification oflibrary search parameters, while variable modifications in-cluded oxidation on methionine (M) and acetylation on theprotein N-terminus. Both precursor and peptide FDR werecontrolled at 1%. For identification of differentiallyexpressed proteins, the fold changes and p value (Student’s t-test) should be> 1.2 or <0.83 and< 0.05, respectively.

2.7. PRM Assays. )e parallel reaction monitoring (PRM)method was used to verify the differentially expressed pro-teins. Peptide separation was performed on the AcclaimPepMap RSLC C18 analytical column using a multistepgradient of B: 2–8% for 1min, 8–28% for 10min, 28–40% for

2 Evidence-Based Complementary and Alternative Medicine

10min, and 40–90% for 1min. )en, B was held at 90% for3min. A flow rate of 300 nL/min was used, and the analysisduration was 60min. ESI source settings were the same asabove. )e PRM analysis included a full MS1 event followedby up to 10 targeted MS2 events. )e m/z scan range of MS1and scanning resolution were set as 350–1500 and 60,000,respectively. AGC target value was set to 3e6, and maximumIT was 45ms. For MS2, scan settings were 15,000 resolvingpower, AGC target 2e5, maximum IT 45ms, normalizedcollision energy 27 eV, and isolation window was set to2.0m/z.

2.8. Bioinformatics Analysis. NCBI BLAST+ (ncbi-blast-2.3.0+) was utilized to compare the gene ontology (GO)annotations of differentially expressed proteins with theappropriate protein sequence database, and the top 10aligned sequences that meet the appropriate E value≤ 1e-3were retained for subsequent analysis. Functional annota-tion was carried out using the online Kyoto Encyclopedia ofGenes and Genomes (KEGG) database (http://geneontology.org/). Both GO and KEGG pathway enrichment analyseswere performed based on Fisher’s exact test. In clusteringanalysis, quantitative data of the target protein set werenormalized first. )en, the software Matplotlib was appliedto classify samples and protein expression in two dimensionsat the same time (Euclidean distance, average linkageclustering).)e hierarchical cluster membership was definedby plotting a heat map.

3. Results

3.1. Protein Identification. Quantitative and SDS-PAGEanalyses showed that the protein was of good quality, suf-ficient, and good in parallelism between samples (Figure S1).Moreover, quantitative normalization was carried out foreach group of samples to ensure the accuracy of massspectrometry results (Figure S2).

A total of 8135 specific peptides and 1152 proteins wereidentified in all samples. )e comparison between thepregnant women with GDM and controls led to an iden-tification of 41 differentially expressed proteins based on theprotein identification criteria; among them, 15 were upre-gulated, while 26 were downregulated in the GDM group(Table 1 and Figure 1).

3.2. PRM Verification. To verify the expression levels ofdifferentially expressed proteins, PRM was employed toanalyze the expression patterns of 41 target proteins in thesame sample. In the experiments, only 12 target proteinswere quantified due to restrictions in the characteristics andexpression abundance of certain proteins (Table 2). Strik-ingly, PRM-based verification obtained consistent resultswith those of the proteomics analysis.

3.3. Bioinformatics Analysis. GO annotations were per-formed to identify biological processes, molecular functions,and cellular components related to the differentially expressed

proteins. As shown in Figure 2(a), the main biological pro-cesses involve the cellular process, metabolic process, bio-logical regulation, response to stimulus, and immune systemprocess, while the main involved molecular functions and cellcomponents include binding and catalytic activities and ex-tracellular region part, cell part, protein-containing complex,and membrane, respectively. Moreover, GO analysis revealedthat the differentially expressed proteins are enriched in eitherbiological processes, such as the metabolism of lipoprotein,fucose, glycoside, and cholesterol, or molecular functionsincluding activities of alpha-L-fucosidase, and arylesterase,and binding of cholesterol and phospholipid or cell com-positions such as high-density lipoprotein particle and cy-toplasmic vesicle membrane (Figure 2(b)). Meanwhile, theKEGG pathway annotation showed that the differentiallyexpressed proteins are enriched in the cholesterol meta-bolism, other glycan degradation, and lysosome (Figure 2(c)).As shown in Figure 3, hierarchical cluster analysis of the twoexperimental groups demonstrated that GDM pathogenesisinvolves dysfunction of numerous pathways.

4. Discussion

Although advanced age, overweight, and obesity have beenshown to be risk factors for GDM [11], its pathogenesisremains elusive. To elucidate the molecular mechanism ofGDM and to search for potential serum markers for earlydiagnosis, we identified and quantitatively analyzed thedifferentially expressed proteins in the serum of pregnantwomen with gestational diabetes and healthy controls. Atotal of 41 differentially expressed proteins were identifiedbetween the two groups. We further showed that theseproteins are correlated with distinct biological processes andsignal transduction pathways, suggesting that developmentof GDM may involve various mechanisms and proteins,including blood coagulation, fibrin clot formation, lipidmetabolism, and immune and inflammatory responses.

Under normal physiological conditions, pregnancy leadsto activation of the coagulation system and fibrinolysissystem, promoting the balance of coagulation function andensuring the stability of the body’s coagulation. Comparedwith normal pregnant women, pregnant women with GDMexhibit more obvious changes in coagulation and fibrino-lysis. )e occurrence of diabetes enhances the activation ofplatelets and coagulation factors [12]. It was reported thatthe levels of prothrombin time and activated partialthrombin time of GDM patients were significantly lowerthan those of the nonpregnant group and healthy pregnancygroup, whereas fibrinogen and plasminogen levels in GDMpatients were markedly increased [13]. Here, we found thatalpha-2-macroglobulin (A2MG) and fibrinogen alpha chain(FIBA) were downregulated in the GDM group. A2MG is abroad-spectrum glycosylation inhibitor of various proteasesincluding blood coagulation-related proteases, which isbelieved to be associated with inflammatory response andcoagulant properties [14, 15]. FIBA is chopped up by theprotease thrombin to generate monomers that are subse-quently polymerized with fibrinogen beta and fibrinogengamma to form an insoluble fibrin matrix [16]. As one of the

Evidence-Based Complementary and Alternative Medicine 3

main components of blood clots, fibrin plays a major role inhemostasis and thrombosis [17], making pregnant womenwith GDM more prone to hypercoagulable state. Further-more, we observed that in the GDM group, coagulationfactors X and XII were upregulated, whereas coagulationfactors including V, XIII, and others were downregulated,albeit no significant difference between the GDM group andcontrols was present. Collectively, these results suggest thatthe apparent hypercoagulability of pregnant women is apotential risk factor for GDM patients.

Poor blood glucose control in diabetic pregnant womenaffects not only the blood coagulation system but also thelipid metabolism [18]. Studies have shown that in patientswith poorly controlled GDM, higher concentrations of high-density lipoproteins and higher triglyceride levels weredetected in the third trimester and the second and thirdtrimesters, respectively. Another study suggested thatAPOA-II, APOC3 [19], APOE, and PON1 [9] might becandidate biomarkers for GDM. Consistent with the abovestudies, we observed significantly higher levels of

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Table 1: )e differentially expressed proteins identified in the different groups.

Protein names Accessions Gene names Fold change P valueENPP2 Q13822 ENPP2 2.41009 0.00099LCAP Q9UIQ6 LNPEP 2.12435 0.02582PSG5 Q15238 PSG5 1.85227 0.03598ADA12 O43184 ADAM12 1.82532 0.02463UBB; UBC; RS27A; RL40 P0CG47; P0CG48; P62979; P62987 UBB; UBC; RP S27A; UBA52 1.77024 0.01534HV70D A0A0C4DH43 IGHV2-70D 1.74216 0.01042PSG11 Q9UQ72 PSG11 1.60007 0.00165FIBA P02671 FGA 1.52207 0.00363CYTC P01034 CST3 1.48321 0.01388APOE P02649 APOE 1.44068 0.03843APOC3 P02656 APOC3 1.38564 0.00234FUCO2 Q9BTY2 FUCA2 1.33303 0.01546GNPTG Q9UJJ9 GNPTG 1.32306 0.00226FUCO P04066 FUCA1 1.31738 0.03303APOL1 O14791 APOL1 1.29031 0.00612PON1 P27169 PON1 0.82963 0.02868IGLC2 P0DOY2 IGLC2 0.82383 0.03519IGLC3 P0DOY3 IGLC3 0.82383 0.03519CR063 Q68DL7 C18orf63 0.81486 0.02107GELS P06396 GSN 0.80875 0.00262ZA2G P25311 AZGP1 0.80875 0.01114DDX55 Q8NHQ9 DDX55 0.78727 0.02054IC1 P05155 SERPING1 0.78424 0.02103APOA4 P06727 APOA4 0.78039 0.00947KV315 P01624 IGKV3-15 0.76482 0.02777LV743 P04211 IGLV7-43 0.73672 0.04679ECM1 Q16610 ECM1 0.73321 0.01806A2MG P01023 A2M 0.71869 0.00313

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Table 2: PRM quantification results.

Protein names Accessions Gene names Quantification type PRM P valueENPP2 Q13822 ENPP2 MS2 2.4777 0.0007LCAP Q9UIQ6 LNPEP MS2 2.1963 0.0232ADA12 O43184 ADAM12 MS2 1.8293 0.0307FIBA P02671 FGA MS2 1.5337 0.0089CYTC P01034 CST3 MS2 1.3124 0.0849APOE P02649 APOE MS1+MS2 1.4429 0.0438APOC3 P02656 APOC3 MS2 1.6852 0.0013APOL1 O14791 APOL1 MS2 1.1492 0.4204ZA2G P25311 AZGP1 MS2 0.9674 0.7255A2MG P01023 A2M MS1+MS2 0.7891 0.0749SAA4 P35542 SAA4 MS2 0.6903 0.0770CETP P11597 CETP MS2 0.7305 0.1371

Evidence-Based Complementary and Alternative Medicine 5

apolipoproteins APOE and APOC3 in the serum of GDMpregnant women. Moreover, we showed that the level ofapolipoprotein APOL1 increased significantly, whereas thelevels of cholesteryl ester transfer protein (CETP) and zinc-alpha-2-glycoprotein (ZA2G) decreased markedly. CETPregulates the reverse transport of cholesterol, by whichexcess cholesterol is removed from surrounding tissues andsent back to the liver for elimination. ZA2G is a noveladipokine related to the lipid metabolism that stimulateslipid degradation in adipocytes and causes extensive fatlosses associated with certain advanced cancers [20]. Mraceket al. [21] suggested that ZA2G has a protective effect on thedevelopment of obesity and is related to insulin resistance.Together, these results support the role of the lipid meta-bolism and transport in GDM and suggest its potential forprediction and diagnosis of GDM.

In this study, we identified ectonucleotide pyro-phosphatase/phosphodiesterase family member 2(ENPP2) as the most upregulated protein in the GDMgroup, which is implicated in obesity-related metabolicphenotypes. It has been reported that increased ENPP2expression in adipose tissue is closely related to impairedglucose homeostasis and/or insulin resistance [22], whileENPP2 gene regulation affects immune cell populationand tissue inflammation in adipose tissue [23]. Althoughstudies have shown that ENPP2 may play a role in in-duction of parturition [24, 25], the relationship betweenENPP2 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 investigatedwhether ENPP2 is involved in GDM pathogenesis and canact as a potential biomarker.

In this study, we performed proteomic analysis of ma-ternal serum samples to identify differentially expressedproteins associated with GDM. A total of 41 proteins dis-played an expression pattern that was significantly differentbetween pregnant women with GDM and normal pregnantwomen. Furthermore, bioinformatics analysis suggested thatthe occurrence and development of GDM may involvemultiple biological and pathological processes, such as bloodcoagulation, fibrin clot formation, lipid metabolism, im-munity, and inflammation. )us, proteins related to theabove processes could have potentials as candidate bio-markers for predicting GDM or as intervention targets forpreventing the development of GDM.

We need to make a statement, this manuscript had everbeen presented as “preprint” in “Research Square,” butwhich had not been published [26].

Data Availability

)e analyzed data are available for noncommercial use fromthe corresponding author upon request.

Ethical Approval

)e study was approved by the Ethical Committee of theFirst Affiliated Hospital of Fujian Medical University.

4.002.671.330.00–1.33–2.67–4.00

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Figure 3: Hierarchical cluster analysis of differentially expressed proteins between the two different groups, including A (GDM pregnantwomen) and B (normal pregnant women).

6 Evidence-Based Complementary and Alternative Medicine

Consent

)e consent to participate of this study have been approved.Written consent to publish this information was obtainedfrom study participants.

Conflicts of Interest

)e authors declare that they have no other conflicts ofinterest.

Authors’ Contributions

Jianhua Li developed the project and wrote the article. Lin Luwrote the article. Xinping Xie analyzed data. Xiaofeng Daicollected data. Shan Zheng revised the article. Lihong Chendeveloped the project. Jianhua Li and Lin Lu contributedequally to this work.

Acknowledgments

)is study was supported by the Guiding Project of FujianScience and Technology Department (2017Y0034) andStartup Fund for Scientific Research, Fujian Medical Uni-versity (2018QH1092).

Supplementary Materials

Figure S1. Quality control of protein samples. Quantitativeand SDS-PAGE analyses showed that the protein was ofgood quality, sufficient, and good in parallelism betweensamples. Figure S2. Before and after quantitative normali-zation of mass spectrometry data of each group. (Supple-mentary Materials)

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