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Proteomics Analysis of Serum Proteins in Gestational Diabetes

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Proteomics Analysis of Serum Proteins in Gestational Diabetes Proteomics Analysis of Serum Proteins in Gestational Diabetes Jianhua Li The First Aliated Hospital of Fujian Medical University Lin Lu The First Aliated Hospital of Fujian Medical University Xinping Xie The First Aliated Hospital of Fujian Medical University Xiaofeng Dai The First Aliated Hospital of Fujian Medical University Lihong Chen ( [email protected] ) First Aliated Hospital of Fujian Medical University https://orcid.org/0000-0001-7552-3533 Shan Zheng The First Aliated 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 identied proteins. Results A total of 1152 quantiable 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 identied 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 dened as a varying degree of glucose tolerance disorder that occurs or is rst 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], inammation [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 identication 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 classication 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 reects 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 inammation. 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 Aliated 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 ltered using a 0.22 µm centrifuge tube to collect the ltrate. The BCA method was used for protein quantication. 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 nal 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 ltrate, 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 quantication. 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). Briey, peptides were rst 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 (Scientic Thermo Fisher, 50um × 15cm). The gradient was comprised of holding at 1% for the rst 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 ow rate of 300 nL/min on an Easy nLC system (Scientic Thermo Fisher). The peptides were analyzed by the electrospray ionization tandem mass spectrometry (ESI-MS/MS) in Q- Exactive HF-X (Scientic 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 dened as a xed modication of library search parameters, while variable modications included oxidation on methionine (M) and acetylation on the protein N-terminus. Both precursor and peptide FDR were controlled at 1%. For identication 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 ow 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.
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