The Differentiation of Proteome Analysis of Omental Adipose Tissue, Placenta and Skeletal Muscle in between Pregnant Women with Gestational Diabetes and Type 1 Diabetes Mellitus

The Differentiation of Proteome Analysis of Omental Adipose Tissue, Placenta and Skeletal Muscle in between Pregnant Women with Gestational Diabetes and Type 1 Diabetes Mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Differentiation of Proteome Analysis of Omental Adipose Tissue, Placenta and Skeletal Muscle in between Pregnant Women with Gestational Diabetes and Type 1 Diabetes Mellitus Zeynep Cantürk, Emre Gezer, Gürler Akpınar, Murat Kasap, Ahmet Yiğit Çakıroğlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4421269/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose The objective of this study was to investigate global changes in protein profiles within omental adipose (OAT), placental (PT), and smooth muscle tissues (SMT), with the aim of identifying potential triggering or affecting biomarkers in gestational (GDM) and type 1 diabetes (T1DM) by comparing them with the control group. Methods Three distinct tissue sources representing the two disease groups and the control group were collected and subjected to comparative proteomic analysis. This comprehensive approach was employed to elucidate the differentially regulated proteins among the groups. Western blot analysis was used to validate the observed changes at the protein level. Results A total of 23 proteins exhibited common alterations, and 18 proteins displayed inverse changes in OAT, PT, and SMT among pregnant women with either GDM or T1DM compared to the control group. Among these 18 differentially expressed proteins, carbonic anhydrase 1 (CA1) and alpha-enolase (ENOA) differed from the others in that they were upregulated in GDM and downregulated in T1DM in the studied tissues compared with controls. Proteomic analyses highlighted alterations in the expression of CA1 protein, a shared feature across all groups. Conclusion Our study marks an inaugural attempt to distinguish proteomic profile changes across diverse tissues in pregnant women diagnosed with GDM and T1DM when compared to healthy controls. The findings of this study could potentially elucidate the underlying pathophysiological mechanisms contributing to the development of GDM, as well as the repercussions of impaired glucose metabolism resulting from both short- and long-term hyperglycemia during pregnancy. Proteomics Gestational Diabetes Type 1 Diabetes Mellitus Pregnancy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Gestational diabetes mellitus (GDM), which is associated with genetic and environmental risk factors, is defined as any level of glucose intolerance diagnosed during pregnancy [ 1 , 2 ]. The estimated prevalence of GDM ranges from 1–14% [ 3 , 4 ]. Insulin resistance is the main pathophysiological mechanism responsible for GDM. GDM develops when pancreatic β-cells cannot function properly and are unable to handle impaired glucose metabolism owing to severe insulin resistance [ 5 ]. Under normal conditions, a metabolic shift from lipid synthesis to lipid oxidation occurs during pregnancy to preserve the adequate supply of glucose to the growing fetus [ 6 ]. In GDM patients, however, elevated plasma glucose levels prevent the shift of metabolism towards lipid oxidation and thus inhibit the production of glucose [ 7 ]. Three body parts are well established to be associated with GDM. The first is skeletal muscle, where lipid oxidation occurs. The second is adipose tissue, which expands in weight, diameter, and thickness during pregnancy. The expansion of adipose tissue can trigger the secretion of pro-inflammatory cytokines and may contribute to the development of GDM [ 8 – 12 ]. The placenta is the third part of the body that becomes heavier and larger in GDM in order to maintain its efficiency to provide the increased nutritional requirements of the fetus [ 13 – 15 ]. Evidence shows that adipose tissue-derived exosomes from pregnant women with GDM can impair placental glucose metabolism [ 16 ]. Several proteomic studies analyzing maternal plasma or urine have been performed to predict the development of GDM [ 17 – 21 ]. In addition, changes in the proteomic profiles of the placenta, adipose tissue, and skeletal muscle have been studied to elucidate the pathogenesis and other consequences of hyperglycemia in GDM [ 13 , 16 , 30 , 22 – 29 ]. However, there are no data in the literature on proteomic analysis of tissues, which was the focus of this study, from pregnant women with T1DM. However, it is not surprising to observe the effect of increased plasma glucose levels in each tissue of the T1DM group, rather than a change in protein expression that contributes to the development of GDM. In the present study, we aimed to differentiate the proteins between three different tissues from GDM and T1DM patients. Therefore, changes in the proteomic profiles of omental adipose (OAT), placental (PT), and skeletal muscle (SMT) tissues collected during delivery from GDM and T1DM patients and healthy controls were investigated. Methods Tissue Sample Collection Pregnant women diagnosed with GDM (n = 11) or T1DM (n = 11) and healthy pregnant controls (n = 11) receiving care at our clinic were included in this study. They were monitored in the Department of Endocrinology, and only patients diagnosed with GDM or T1DM based on blood tests were included in the study. Informed consent was obtained from each patient and the study was approved by the Institutional Ethics Committee. Placental, omental, and smooth muscle tissues were collected from both GDM and T1DM patients and from healthy controls during cesarean section. Five punch biopsies from various areas of each tissue were randomly pooled immediately after cesarean section and washed in cold PBS to eliminate any contaminating blood. The tissues were then frozen in liquid nitrogen and stored at -80°C until use [ 31 ]. The clinical and biochemical properties of the patients and controls are presented in Table 1 . Table 1 Clinical characteristics of the study groups. Values are presented as mean ± S.E.M.. GDM (n = 11) Type 1 DM (n = 11) Control (n = 11) p- value Maternal age (Years) 33.7 ± 2.5 31.7 ± 4.4 30.1 ± 4.1 0.93 Maternal BMI at 12 weeks (kg/m2) 32.9 ± 3.7 30.2 ± 4.5 30.0 ± 4.5 0.21 Maternal BMI at delivery (kg/m2) 33.7 ± 3.6 32.3 ± 3.5 34.2 ± 3.0 0.38 Gestational age at birth (weeks) 37.0 ± 0.4 36.1 ± 1.6 38.2 ± 0.4 b, c Fetal birth weight (g) 3234 ± 395 3140 ± 407 3160 ± 520 0.87 Fetal gender 6 F; 5 M 6 F; 5 M 5 F; 6 M NS Gravida 2.1 ± 0.8 1.9 ± 0.7 2.1 ± 0.5 0.77 Parity 0.8 ± 0.7 0.7 ± 0.7 1.1 ± 0.5 0.39 Maternal OGTT at 28 weeks of gestation Fasting plasma OGTT (mg/dl) 90.9 ± 12.9 ND 82.3 ± 6.8 0.63 1-h plasma OGTT (mg/dl) 187.6 ± 20.5 ND 126.6 ± 20.4 b 2-h plasma OGTT (mg/dl) 158.7 ± 20.0 ND 112.5 ± 9.6 b Maternal OGTT at 6 weeks of postpartum Fasting plasma OGTT (mg/dl) 86.1 ± 10.0 ND ND - 1-h plasma OGTT (mg/dl) 169.6 ± 14.9 ND ND - 2-h plasma OGTT (mg/dl) 147.4 ± 21.6 ND ND - Hemoglobin A1C 5.5 ± 0.3 5.8 ± 0.2 ND 0.3, a NS, not significant; ND, not done; OGTT, oral glucose tolerance test; F, female; M, male. a, statistically significant between groups 1 and 2; b, statistically significant between groups 1 and 3; c, statistically significant between groups 2 and 3 (p < 0.05). Protein extraction Tissue samples from patients were diced on ice in a sterile glass culture plate and washed three times with an ample amount of ice-cold washing buffer (10 mM Tris-HCl, pH 7.0, 250 mM sucrose) to remove excess blood. After 10 min of centrifugation at 4°C at 2000 ×g, excess wash buffer was decanted, and 250 µl of buffer containing 7 M urea, 2 M thiourea, 4% CHAPS, 30 mM Tris pH 8.5, and 1x protease inhibitor cocktail was added to each tissue pellet. The tissue pellets were lysed using 0.5 mm stainless steel beads with the help of a homogenizer (Next Advance, Averill Park, New York, USA) at + 4ºC. The supernatant containing the soluble protein extract was obtained by centrifugation at 20,000 g for 30 min at 4°C. Protein concentration was determined using the Bradford assay with a BSA standard (Bio-Rad, Hercules, California, USA). The soluble protein-containing supernatants were stored in Lo-bind tubes (Eppendorf, Hamburg, Germany) at − 80°C until analysis. Preparation of pooled samples Equal amounts of protein from each sample were combined in a single tube, and the protein concentration of the pooled samples was re-measured. The protein concentration estimated by measurement was compared with the calculated protein concentration to validate that the samples were correctly pooled. Further validation was achieved using SDS-PAGE, followed by visual examination of the protein profiles. Minimal protein labeling and the difference gel electrophoresis (DIGE) For DIGE experiments, protein samples were prepared in minimal DIGE lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS w/v, 30mM Tris-HCl, pH 8.5), and equal amounts of proteins were pooled for each group. DIGE-specific Cy2, Cy3, and Cy5 dyes (Life Tech, USA) were used for labeling of 50 µg pooled protein samples in each experiment. A separate DIGE experiment was designed for each tissue type (OAT, PT, and SMT), and the tissues were marked with Cye dyes according to the experimental group they belonged to. Protein pools obtained from the OAT, PT, and SMT tissues were labeled with Cy3 for the GDM group, Cy5 for the T1DM group, and Cy2 for the control group. Two-dimensional gel electrophoresis (2DE) For first-dimensional separation via isoelectric focusing (IEF), 750 µg of each pooled protein sample was loaded onto immobilized pH gradient strips (IPG) (17 cm, pH 3–10) via passive rehydration at 20°C for 16 h. Isoelectric focusing was performed using a Protean isoelectric focusing cell (Bio-Rad, Hercules, California, USA). Following the focusing step, the IPG strips were loaded directly onto 1 mm-thick 12% sodium dodecyl sulfate-polyacrylamide gels. After electrophoresis, the gels were stained with colloidal Coomassie Blue (Bio-Rad, Hercules, California, USA). Two separate gels were run for each pooled group to minimize experimental variation. Image analysis of 2DE and DIGE gels Conventional 2DE gels were visualized with VersaDoc MP4000 (Bio-Rad, USA), and PDQuest 2DE Analysis Software (Bio-Rad, USA) was used for spot intensity calibration, spot detection, and background subtraction. DIGE gels were also visualized with VersaDoc MP4000 (Bio-Rad, Hercules, California, USA) using three different light sources. The number of spots was normalized to the total valid spot intensity by using a linear regression model. Paired Student’s t-tests were used to assess differences in the average protein abundance between the gels. 2D gel image comparison and protein spot intensities with more than a two-fold significant change (p < 0.05) in a consistently increased or decreased pattern were considered differentially expressed. Inter-experimental comparison between 2DE and DIGE experiments To perform an inter-experimental comparison between the conventional 2DE and DIGE experiments, “ the compare experiment wizard ” implemented in PD-Quest Advance software was used. Two master gels created during analysis of the gel images from each independent experimental study were compared, and overlapping differentially regulated protein spots were cut and identified by MALDI-TOF/TOF analysis. Protein identification by MALDI-TOF/TOF . Protein identification experiments were performed at the Kocaeli University Medical Biology Proteomics Laboratory using the ABSCIEX MALDI-TOF/TOF 5800 system (Applied Biosystems, Framingham, MA, USA). Spots of interest were subjected to in-gel tryptic digestion using an in-gel digestion kit, following the manufacturer’s protocol (Pierce, USA). Before deposition onto a MALDI plate, all samples were desalted with 10 µl ZipTipC18 (Millipore, USA). The TOF spectra were recorded in the positive ion reflector mode with a mass range from 400 to 2000 Da. The spectra were calibrated using the trypsin autodigestion ion peak m/z (842.510 and 2211.1046) as internal standards. Ten of the strongest peaks in the TOF spectra per sample were chosen for MS/MS analysis. All of the PMFs were searched in the MASCOT version 2.5 (Matrix Science) using the streamline software ProteinPilot (ABSCIEX, USA), with the following criteria: SWISSPROT database; species restriction to H. sapiens; enzyme of trypsin; at least ten independent peptides matched; at most one missed cleavage site; MS tolerance set to ± 50 ppm and MS/MS tolerance set to ± 0.2 Da; fixed modification being carbamidomethyl (Cys) and variable modification being oxidation (Met); peptide charge of + 1 and monoisotopic. Only significant hits, as defined by the MASCOT probability analysis (P < 0.05), were accepted. A protein-protein interaction (PPI) network of the identified proteins was constructed using the online analysis tool STRING v10.0. Validation of differentially regulated proteins using western blot analysis. The identified proteins were validated by western blot analysis using specific primary antibodies against carbonic anhydrase (1:500, Santa Cruz, sc-393490), superoxide dismutase (1 : 1000, Santa Cruz, sc-271014), glyceraldehyde-3-phosphate dehydrogenase (1:1,000, Santa Cruz, sc-47724), alpha enolase (1 : 5000, Santa Cruz, sc-101513), creatine kinase-M (1 : 5000, Santa Cruz, sc-69848), and β-actin (1:2,500, Santa Cruz, sc-8432). β-Actin was used as an internal control to ensure equal protein loading. The membranes were visualized with an ECL detection system (Bio-Rad, USA) using X-ray films and a VersaDoc imaging system (Bio-Rad, USA). Band intensities were quantified using the Quantity One 1D image analysis software (Bio-Rad, USA). Western blot analyses were performed only once for each antibody to confirm the changes in the protein spot intensities observed in the 2D gels. Statistical analysis The statistical significance of image analysis was determined by the Student’s t-test (statistical level of p < 0.05). Results For proteomic analysis, two complementary approaches, DIGE and 2DE, were used. The experimental workflow is shown in Fig. 1 . DIGE analysis revealed the presence of 230, 217, and 220 well-separated protein spots on the gels produced from the OAT, PT, and SMT protein pools, respectively (Fig. 2 ). For the OAT tissues, we found that 21 protein spots were differentially regulated in the GDM and T1DM groups compared with the control. Using a similar analysis, we detected 17 protein spots in PT tissues and 18 protein spots in SMT. A similar comparative protein spot analysis was performed using 2DE gels (Fig. 3 ). Each experimental group displayed spot distribution patterns with high spot matching rates (>95%). The average number of protein spots for the OAT, PT, and SMT groups was 420 ± 17 (OAT), 407 ± 20(PT), and 412 ± 14 (SMT), respectively. For the OAT tissues, when we performed comparative 2DE gel analysis, we found that the shared 27 protein spots were differentially regulated in both GDM vs. control and T1DM vs. control. Using a similar analysis, we detected 22 protein spots in PT tissues and 19 protein spots in SM tissues (Fig. 1 ). For complementation purposes, differentially regulated protein spots from the DIGE and 2DE gels were compared. Protein spots that were common to both experiments were considered more reliable than those that were differentially regulated only in one of the experimental approaches. For the OAT tissues, we found that 18 proteins were shared in both the DIGE and 2DE experiments. Using a similar method, we detected 15 shared proteins in PT tissues and 12 shared proteins in SMT tissues (Fig. 1 ). The ratios of regulation relative to the control for the proteins shared within each tissue, along with the Mass Spectrometry (MS) identification parameters, are described in detail in Table 2 . Table 2 The ratios of regulation relative to the control for the proteins shared within each tissue AC no. Protein Acc. Mw (kd) MS/MS Score Expect Matches pI Seq. Cov. (%) Protein Description GDM/Control T1DM/Control Omental adipose tissue P27797 CALR 48112 173 1,00E-13 16 4,29 26% Calreticulin ↑ 2,07 ↑ 1,22 P02647 APOA1 30759 380 2,00E-34 30 5,56 58% Apolipoprotein A-I ↑ 7,95 ↑ 7,3 P07237 PDIA1 57081 423 1,00E-38 36 4,76 54% Protein disulfide-isomerase ↑ 1,28 ↓ 0,54 P09525 ANXA4 35860 375 6,40E-34 28 5,84 49% Annexin A4 ↑ 2,24 ↓ 0,98 P02675 FIBB 55892 504 8,10E-47 36 8,54 46% Fibrinogen beta chain ↑ 4,91 ↑ 1,11 P05787 K2C8 53671 291 1,60E-25 29 5,52 36% Keratin, type II cytoskeletal 8 ↑ 3,05 ↑ 1,87 Q9UMS4 PRP19 55146 68 0,003 16 6,14 25% Pre-mRNA-processing factor 19 ↑ 4,67 ↑ 6,41 Q8N7Z2 GG6L1 77058 16 4,60E + 02 9 5,29 9% Golgin subfamily A member 6-like protein 1 ↓ 0,45 ↓ 0,5 P02749 APOH 38273 58 0,035 8 8,34 11% Beta-2-glycoprotein 1 ↑ 1,62 ↓ 1,17 P00352 AL1A1 54827 306 5,10E-27 23 6,3 31% Aldehyde dehydrogenase 1A1 ↓ 0,43 ↓ 0,41 P00915 CA1 28852 397 4,00E-36 17 6,59 59% Carbonic anhydrase 1 ↑ 1,47 ↓ 0,95 P02511 CRYAB 20146 406 5,10E-37 24 6,76 57% Alpha-crystallin B chain ↑ 2,24 ↑ 2,2 P04179 SODM 24707 492 1,30E-45 21 8,35 35% Superoxide dismutase [Mn], mitochondrial ↓ 0,88 ↓ 0,4 P68871 HBB 15988 473 1,00E-43 19 6,75 84% Hemoglobin subunit beta ↓ 0,87 ↓ 0,64 P01834 IGKC 11602 116 5,10E-08 9 5,58 75% Ig kappa chain C region ↑ 1,72 ↑ 2,43 P04406 G3P 36030 234 8,10E-20 17 8,57 25% Glyceraldehyde-3-phosphate dehydrogenase ↑ 2,35 ↓ 0,82 P07355 ANXA2 38580 551 1,60E-51 36 7,57 63% Annexin A2 ↑ 1,82 ↑ 1,35 P02671 FIBA 94914 424 8,10E-39 30 5,7 16% Fibrinogen alpha chain ↑ 1,2 ↓ 0,5 Plecental tissue P14625 ENPL 92411 38 3 9 4,76 8% Endoplasmin ↑ 2,23 ↑ 2,44 P27797 CALR 48112 294 8,10E-26 24 4,29 43% Calreticulin ↑ 1,16 ↓ 0,42 P14314 GLU2B 59388 225 6,40E-19 24 4,33 27% Glucosidase 2 subunit beta ↑ 1,28 ↑ 1,5 P21980 TGM2 77280 492 1,30E-45 41 5,11 47% Protein-glutamine gamma-glutamyltransferase 2 ↓ 0,46 ↓ 0,39 P04899 GNAI2 40425 323 1,00E-28 33 5,34 54% Guanine nucleotide-binding protein G(i) subunit alpha-2 ↓ 0,52 ↓ 0,23 P14061 DHB1 34958 237 4,00E-20 25 5,47 52% Estradiol 17-beta-dehydrogenase 1 ↓ 0,06 ↑ 2,33 P04792 HSPB1 22768 355 6,40E-32 22 5,98 52% Heat shock protein beta-1 ↑ 1,23 ↑ 10,7 P13010 KU86 82652 278 3,20E-24 34 5,55 29% ATP-dependent DNA helicase 2 subunit 2 ↓ 0,48 ↑ 1,46 P30048 PRDX3 27675 244 8,10E-21 16 7,67 45% Thioredoxin-dependent peroxide reductase, mitochondrial ↑ 2,12 ↑ 1,87 P00915 CA1 28852 311 1,60E-27 18 6,59 57% Carbonic anhydrase 1 ↑ 1,09 ↓ 0,28 P06733 ENOA 47139 370 2,00E-33 27 7,01 39% Alpha-enolase ↑ 1,2 ↓ 0,34 P02545 LMNA 74095 493 1,00E-45 48 6,57 57% Lamin-A/C ↓ 0,36 ↑ 2,11 P04179 SODM 24707 392 1,30E-35 20 8,35 35% Superoxide dismutase [Mn], mitochondrial ↓ 0,93 ↓ 0,32 Q07955 SFRS1 27728 126 5,10E-09 14 10,37 35% Splicing factor, arginine/serine-rich 1 ↑ 8,95 ↑ 1,01 P05108 CP11A 60064 609 2,60E-57 34 8,89 43% Cholesterol side-chain cleavage enzyme, mitochondrial ↓ 0,26 ↑ 2,09 Smooth muscle tissue P04792 HSPB1 22768 267 4,00E-23 15 5,98 47% Heat shock protein beta-1 ↓ 0,59 ↓ 0,54 P02675 FIBB 55892 508 3,20E-47 37 8,54 46% Fibrinogen beta chain ↓ 0,44 ↑ 1,5 P12883 MYH7 222959 373 1,00E-33 43 5,63 14% Myosin-7 ↑ 1,26 ↓ 0,1 P35609 ACTN2 103788 537 4,00E-50 51 5,31 43% Alpha-actinin-2 ↑ 2,02 ↓ 0,21 O14558 HSPB6 17125 288 3,20E-25 13 5,95 56% Heat shock protein beta-6 ↑ 1,02 ↑ 2,64 P06733 ENOA 47139 371 1,60E-33 29 7,01 51% Alpha-enolase ↑ 1,2 ↓ 0,11 P00915 CA1 28852 286 5,10E-25 19 6,59 68% Carbonic anhydrase 1 ↓ 0,29 ↓ 0,36 P45378 TNNT3 31805 484 8,10E-45 33 5,71 52% Troponin T, fast skeletal muscle ↓ 0,98 ↓ 0,22 P06732 KCRM 43074 447 4,00E-41 33 6,77 60% Creatine kinase M-type ↑ 1,13 ↓ 0,15 P11217 PYGM 97031 739 2,60E-70 55 6,57 51% Glycogen phosphorylase, muscle form ↓ 0,81 ↓ 0,29 Q9NP98 MYOZ1 31725 258 3,20E-22 23 8,86 63% Myozenin-1 ↑ 1,23 ↓ 0,21 P13929 ENOB 46902 416 5,10E-38 35 7,59 54% Beta-enolase ↑ 1,05 ↓ 0,36 To verify the regulatory trends, western blot analyses were performed for some of the selected proteins, namely mitochondrial superoxide dismutase (SODM) and Glyceraldehyde-3-phosphate dehydrogenase (G3P) in the omental tissue, alpha-enolase (ENOA) and SODM in the placental tissue, and ENOA and creatine kinase M-type (KCRM) in the smooth muscle tissue. For the selected proteins, close-up images of their corresponding spots were produced to confirm the regulatory trends (Fig. 4 ). Western blotting was performed and analyzed (Fig. 5 ). The regulation trends observed in the 2DE and DIGE experiments matched those observed in the western blots. In the investigation of regulatory patterns across experimental groups (GDM versus control or T1DM versus control), the comparative analysis of differentially regulated proteins in omental adipose, placental, and smooth muscle tissues revealed intriguing insights. Carbonic anhydrase 1 (CA1) emerged as the singular protein that was consistently shared among all experimental groups (Fig. 6 ). Analysis of close-up images of CA1 in 2DE gels showed that the protein was downregulated in all T1DM tissue samples compared with controls. In GDM patients, however, CA1 was upregulated in OAT and PT samples, while it was downregulated in SMT samples compared to controls. Discussion In this study, we aimed to investigate the differences in protein expression associated with GDM and T1DM across three distinct tissue samples: the omentum, placenta, and skeletal muscle. Our primary objective was to identify proteins that exhibit inverse expression patterns in at least two tissue samples from each group, with the intention of distinguishing between proteomic alterations in GDM and T1DM. When comparing the three distinct tissue types in patients with GDM and T1DM and their respective control groups, we observed significant differences in the protein expression profiles. Specifically, six proteins (carbonic anhydrase 1, alpha-enolase, calreticulin, fibrinogen beta chain, mitochondrial superoxide dismutase, and heat shock protein beta-1) exhibited distinctive expression patterns in both groups (Fig. 6 ). Notably, among these six proteins, only two demonstrated contrasting trends in the two different tissue samples within the GDM and T1DM groups. An inverse expression pattern of CA1 was consistently observed in both OAT and PT, whereas the differentiation pattern of ENOA was consistent in PT and SMT. These two proteins were upregulated in two distinct samples from GDM patients, but downregulated in the corresponding samples from T1DM patients. Carbonic anhydrase (CA) is a zinc metalloenzyme that catalyzes the conversion of CO2 to HCO3 − and H + in various organisms. In mammals, 14 different isoforms of this zinc metalloenzyme have been identified, and they are found in various cellular compartments, including the cytosol, mitochondria, and cell membrane [ 32 ]. Among the cytosolic isoforms, CA1 plays a significant role in multiple physiological processes, such as the regulation of acid-base balance, ion transport, and gas exchange in numerous organs and tissues. Disruptions in the function of this enzyme have been linked to the pathophysiology of several diseases, including insulin resistance and diabetes mellitus. In a case-control study involving 86 participants; it was observed that CA activity was elevated in individuals with insulin resistance when compared to the control group [ 33 ]. One of CA's crucial roles of CA is to provide HCO3- as a substrate for the initial step in hepatic gluconeogenesis, which was a key finding in this study [ 34 ]. Furthermore, Kondo et al . reported an increased level of CA1 in patients with diabetes mellitus, indicating its potential significance in this disease [ 35 ]. From a different perspective, various clinical trials have consistently reported that elevated plasma glucose levels are associated with a decrease in erythrocyte carbonic anhydrase (CA) activity [ 35 , 36 ]. This reduction in enzymatic activity is attributed to the glycosylation of CA, a process initiated by increased exposure of erythrocytes to elevated glucose levels in our current study. We observed upregulation of CA1 expression in OAT and PT samples from individuals with GDM, while downregulation of CA1 was noted in the same tissues from patients with T1DM. Considering the previously discussed mechanisms, our findings suggest that OAT and PT may play pivotal roles in the development of impaired glucose metabolism in pregnant women, potentially reflecting the acute impact of hyperglycemia. Conversely, the persistent effects of hyperglycemia in T1DM were evident in these two tissues. Furthermore, the consistent downregulation of CA1 in SMTs from both GDM and T1DM groups provides compelling evidence that smooth muscle is a shared tissue in which the effects of both acute and chronic hyperglycemia manifest. Alpha-enolase (ENOA) is a widely distributed metalloenzyme found in the cytoplasm, nucleus, and cell surface [ 37 ]. Among its various functions, ENOA plays a crucial role in the glycolytic pathway, catalyzing the conversion of 2-phosphoglyceric acid to phosphoenolpyruvic acid, particularly in response to stress. Several proteomic studies have reported that ENOA is differentially expressed before and after stress exposure [ 38 ]. Notably, Lu et al . observed significant upregulation of ENOA expression in the liver and heart of diabetic rats, which was interpreted as a protective response to counteract oxidative and nitrative stress associated with diabetes, aimed at preventing tissue damage [ 39 , 40 ]. In another study, the authors reported an increase in ENOA expression in the poor glycemic control group [ 41 ]. These findings underscore the significance of ENOA in the context of stress responses and its potential implications for diabetes-related complications. Consistent with the existing literature, our study revealed upregulation of ENOA expression in the PT and SMT of the GDM group. This finding underlines the likelihood that impaired glucose metabolism in pregnant women with GDM may influence placental tissue as well as SMT. Notably, we observed a downregulation of ENOA in the placental and SMT samples from the Type 1 Diabetes Mellitus (T1DM) group, which suggests that chronic hyperglycemia may compromise acute compensatory mechanisms, such as the upregulation of ENOA, in patients with T1DM. A limitation of our study pertains to the comparison of protein results derived from distinct tissue types. To address this concern, we adopted an approach in which, rather than directly comparing the 2-dimensional electrophoresis (2DE) gel profiles between groups, we focused on comparing the identified proteins within each group to evaluate variations across different groups. This strategy allowed us to mitigate the challenges associated with inter-tissue differences, while facilitating a more precise assessment of protein variations between groups. In conclusion, our study identified 23 proteins that exhibited common alterations and 18 proteins that displayed inverse changes in OAT, PT, and SMT among pregnant women with either GDM or T1DM when compared to a control group. Among these 18 differentially expressed proteins, CA1 and ENOA differed from the others in that they were upregulated in GDM and downregulated in T1DM in at least two different tissues compared to the controls. CA1 and ENOA exhibited up-regulation in GDM and down-regulation in T1DM compared to controls in the AOT - PT samples, and PT - SMT samples, respectively. To the best of our knowledge, this study marks an inaugural attempt to distinguish proteomic profile changes across diverse tissues in pregnant women diagnosed with GDM and T1DM when compared to healthy controls. Our results might help shed light on proteomic alterations that could potentially elucidate the underlying pathophysiological mechanisms contributing to the development of GDM, as well as the repercussions of impaired glucose metabolism resulting from both short-term (newly developed GDM) and long-term (T1DM) hyperglycemia during pregnancy. Declarations Funding: This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 114S412. The funding body had no role in the design or execution of this study. Ethics Approval : This study was approved by the Ethics Committee of Kocaeli University (approval number: KOU KAEK 2013/79). Conflict of Interest : The authors report no conflict of interest. Consent to Participate : Informed consent was obtained from all individual participants included in the study. Data Availability Statement: The data that support the findings of this study are available on request from the corresponding author. Authors’ Contributions: The study conception and experimental design were performed by MK, GA, and ZC. Tissue collection with surgery and storage was performed by AYC and ZC. Sample preparation and protein isolation were performed by EG, MK, and GA. The experiments were performed by EG, GA, and MK. Bioinformatics analysis was performed by GA and MK. Clinical evaluation of the data was performed by ZC and YC. The first draft of the manuscript was written by GA and EG. Writing, review, and editing of the draft were performed by GA and MK. All authors commented on the previous versions of the manuscript. All authors have read and approved the final manuscript. References Association AD (2009) Diagnosis and classification of diabetes mellitus. Diabetes Care 32:S62–S67 Haghvirdizadeh P, Mohamed Z, Abdullah NA, et al (2015) KCNJ11: Genetic Polymorphisms and Risk of Diabetes Mellitus. 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Am J Physiol - Endocrinol Metab 282:. https://doi.org/10.1152/ajpendo.00124.2001 Catalano PM, Huston L, Amini SB, Kalhan SC (1999) Longitudinal changes in glucose metabolism during pregnancy in obese women with normal glucose tolerance and gestational diabetes mellitus. In: American Journal of Obstetrics and Gynecology. Mosby Inc., pp 903–916 Atègbo JM, Grissa O, Yessoufou A, et al (2006) Modulation of adipokines and cytokines in gestational diabetes and macrosomia. J Clin Endocrinol Metab 91:4137–4143. https://doi.org/10.1210/jc.2006-0980 Lappas M (2014) Effect of pre-existing maternal obesity, gestational diabetes and adipokines on the expression of genes involved in lipid metabolism in adipose tissue. Metabolism 63:250–262. https://doi.org/10.1016/j.metabol.2013.10.001 Rojas-Rodriguez R, Lifshitz LM, Bellve KD, et al (2015) Human adipose tissue expansion in pregnancy is impaired in gestational diabetes mellitus. Diabetologia 58:2106–2114. https://doi.org/10.1007/s00125-015-3662-0 Sogabe M, Okahisa T, Hibino S, Yamanoi A (2012) Usefulness of differentiating metabolic syndrome into visceral fat type and subcutaneous fat type using ultrasonography in Japanese males. J Gastroenterol 47:293–299. https://doi.org/10.1007/s00535-011-0489-4 Donohoe CL, Doyle SL, Reynolds J V. (2011) Visceral adiposity, insulin resistance and cancer risk. Diabetol. Metab. Syndr. 3 Liu B, Xu Y, Voss C, et al (2012) Altered Protein Expression in Gestational Diabetes Mellitus Placentas Provides Insight into Insulin Resistance and Coagulation/Fibrinolysis Pathways. PLoS One 7:1–12. https://doi.org/10.1371/journal.pone.0044701 Daskalakis G, Marinopoulos S, Krielesi V, et al (2008) Placental pathology in women with gestational diabetes. Acta Obstet Gynecol Scand 87:403–407. https://doi.org/10.1080/00016340801908783 Desoye G, Hauguel-De Mouzon S (2007) The human placenta in gestational diabetes mellitus: The insulin and cytokine network. Diabetes Care 30:S120–S126. https://doi.org/10.2337/dc07-s203 Jayabalan N, Lai A, Ormazabal V, et al (2019) Adipose Tissue Exosomal Proteomic Profile Reveals a Role on Placenta Glucose Metabolism in Gestational Diabetes Mellitus. J Clin Endocrinol Metab 104:1735–1752. https://doi.org/10.1210/jc.2018-01599 Kim SM, Park JS, Norwitz ER, et al (2012) Identification of proteomic biomarkers in maternal plasma in the early second trimester that predict the subsequent development of gestational diabetes. Reprod Sci 19:202–209. https://doi.org/10.1177/1933719111417889 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 (Brooklyn) 34:517–525. https://doi.org/10.21873/invivo.11803 Zhao D, Shen L, Wei Y, et al (2017) Identification of candidate biomarkers for the prediction of gestational diabetes mellitus in the early stages of pregnancy using iTRAQ quantitative proteomics. Proteomics - Clin Appl 11:1–32. https://doi.org/10.1002/prca.201600152 Ravnsborg T, Svaneklink S, Andersen LLT, et al (2019) First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus. PLoS One 14:1–13. https://doi.org/10.1371/journal.pone.0214457 Guo Y, Han Z, Guo L, et al (2018) Identification of urinary biomarkers for the prediction of gestational diabetes mellitus in early second trimester of young gravidae based on iTRAQ quantitative proteomics. Endocr J 65:727–735. https://doi.org/10.1507/endocrj.EJ17-0471 Ramachandrarao SP, Hamlin AA, Awdishu L, et al (2016) Proteomic analyses of Urine Exosomes reveal New Biomarkers of Diabetes in Pregnancy. Madridge J Diabetes 1:11–22. https://doi.org/10.18689/mjd-1000103 Liao Y, Xu GF, Jiang Y, et al (2018) Comparative proteomic analysis of maternal peripheral plasma and umbilical venous plasma from normal and gestational diabetes mellitus pregnancies. Med (United States) 97:. https://doi.org/10.1097/MD.0000000000012232 Liu X, Sun J, Wen X, et al (2020) Proteome profiling of gestational diabetes mellitus at 16-18 weeks revealed by LC-MS/MS. J Clin Lab Anal 34:1–10. https://doi.org/10.1002/jcla.23424 Oliva K, Barker G, Rice GE, et al (2013) 2D-DIGE to identify proteins associated with gestational diabetes in omental adipose tissue. J Endocrinol 218:165–178. https://doi.org/10.1530/JOE-13-0010 Ma Y, Gao J, Yin J, et al (2016) Identification of a Novel Function of Adipocyte Plasma Membrane-Associated Protein (APMAP) in Gestational Diabetes Mellitus by Proteomic Analysis of Omental Adipose Tissue Herrera-Van Oostdam AS, Salgado-Bustamante M, López JA, et al (2019) Placental exosomes viewed from an “omics” perspective: Implications for gestational diabetes biomarkers identification. Biomark Med 13:675–684. https://doi.org/10.2217/bmm-2018-0468 Lapolla A, Porcu S, Roverso M, et al (2013) A preliminary investigation on placenta protein profile reveals only modest c hanges in well controlled gestational diabetes mellitus. Eur J Mass Spectrom 19:211–223. https://doi.org/10.1255/ejms.1225 Assi E, D’Addio F, Mandò C, et al (2020) Placental proteome abnormalities in women with gestational diabetes and large-for-gestational-age newborns. BMJ Open Diabetes Res Care 8:1–8. https://doi.org/10.1136/bmjdrc-2020-001586 Boyle KE, Hwang H, Janssen RC, et al (2014) Gestational diabetes is characterized by reduced mitochondrial protein expression and altered calcium signaling proteins in skeletal muscle. PLoS One 9:. https://doi.org/10.1371/journal.pone.0106872 Gharesi-Fard B, Zolghadri J, Kamali-Sarvestani E (2010) Proteome Differences of Placenta Between Pre-Eclampsia and Normal Pregnancy. Placenta 31:121–125. https://doi.org/10.1016/j.placenta.2009.11.004 Mori K, Ogawa Y, Ebihara K, et al (1999) Isolation and characterization of CA XIV, a novel membrane-bound carbonic anhydrase from mouse kidney. J Biol Chem 274:15701–15705. https://doi.org/10.1074/jbc.274.22.15701 Biswas UK, Kumar A (2012) Study on the changes of carbonic anhydrase activity in insulin resistance and the effect of methylglyoxal. J Pak Med Assoc 62:417–421 Ismail IS (2018) The Role of Carbonic Anhydrase in Hepatic Glucose Production. Curr Diabetes Rev 14:108–112. https://doi.org/10.2174/1573399812666161214122351 Kondo T, Murakami K, Ohtsuka Y, et al (1987) Estimation and characterization of glycosylated carbonic anhydrase I in erythrocytes from patients with diabetes mellitus. Clin Chim Acta 166:227–236. https://doi.org/10.1016/0009-8981(87)90425-6 Abel P, Wussow S, Blücher H, et al (1997) Erythrocyte carbonic anhydrase activity in smokers and in diabetic patients. Exp Clin Endocrinol Diabetes 105:17–19. https://doi.org/10.1055/s-0029-1211788 Pancholi V, Fischetti VA (1998) Α-Enolase, a Novel Strong Plasmin(Ogen) Binding Protein on the Surface of Pathogenic Streptococci. J Biol Chem 273:14503–14515. https://doi.org/10.1074/jbc.273.23.14503 Ji H, Wang J, Guo J, et al (2016) Progress in the biological function of alpha-enolase. Anim Nutr 2:12–17. https://doi.org/10.1016/j.aninu.2016.02.005 Lu N, Zhang Y, Li H, Gao Z (2010) Oxidative and nitrative modifications of α-enolase in cardiac proteins from diabetic rats. Free Radic Biol Med 48:873–881. https://doi.org/10.1016/j.freeradbiomed.2010.01.010 Lu N, Li J, He Y, et al (2014) Nitrative modifications of α-enolase in hepatic proteins from diabetic rats: The involvement of myeloperoxidase. Chem Biol Interact 220:12–19. https://doi.org/10.1016/j.cbi.2014.05.021 Soongsathitanon J, Umsa-Ard W, Thongboonkerd V (2019) Proteomic analysis of peripheral blood polymorphonuclear cells (PBMCs) reveals alteration of neutrophil extracellular trap (NET) components in uncontrolled diabetes. Mol Cell Biochem 461:1–14. https://doi.org/10.1007/s11010-019-03583-y Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4421269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314482147,"identity":"b34a987d-bbde-4558-9ba9-fe0bcc35cb3a","order_by":0,"name":"Zeynep Cantürk","email":"","orcid":"","institution":"Kocaeli University School of Medicine: Kocaeli Universitesi Tip Fakultesi","correspondingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"","lastName":"Cantürk","suffix":""},{"id":314482148,"identity":"e44cb190-3d66-43a0-9daa-20f32d3a6d74","order_by":1,"name":"Emre Gezer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYHAD5mNgio2deC1saQwMCUCKmXgtPGZgLQyEtMi7tz98XMFgE23e3vPtwccf2+T5mBkYP3zMwa3F8MyBZMMzDGm5c86c3W44I+G2YRszA7PkzG14tMxIOCbZwHA4d4ZE7jZpnoTbjEAtbMy8+LTMf9j+s4Hhf+4M+TfPQFrsCWqRl2BmY2xgOAC0hYcNpCWRoBYDnjRmoMOSc2fwpJlJzki7ndzGzNiM1y/y7ccffmxgsMudwX74mcQHm9u289ubD374iM+WA0CC8R+KGNCh+IA8fulRMApGwSgYBUAAAKu+SzDNKPxHAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5340-6106","institution":"Kocaeli Universitesi Tip Fakultesi","correspondingAuthor":true,"prefix":"","firstName":"Emre","middleName":"","lastName":"Gezer","suffix":""},{"id":314482149,"identity":"604e8443-5ebc-4cc4-8b42-9c60c53ef13f","order_by":2,"name":"Gürler Akpınar","email":"","orcid":"","institution":"Kocaeli University School of Medicine: Kocaeli Universitesi Tip Fakultesi","correspondingAuthor":false,"prefix":"","firstName":"Gürler","middleName":"","lastName":"Akpınar","suffix":""},{"id":314482150,"identity":"40461704-159c-4139-b5a7-f3d0b53363e7","order_by":3,"name":"Murat Kasap","email":"","orcid":"","institution":"Kocaeli University School of Medicine: Kocaeli Universitesi Tip Fakultesi","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Kasap","suffix":""},{"id":314482151,"identity":"b0e5fe48-3b7d-417f-b948-60be3cc0f9d7","order_by":4,"name":"Ahmet Yiğit Çakıroğlu","email":"","orcid":"","institution":"Acıbadem University School of Medicine: Acibadem Universitesi Tip Fakultesi","correspondingAuthor":false,"prefix":"","firstName":"Ahmet","middleName":"Yiğit","lastName":"Çakıroğlu","suffix":""}],"badges":[],"createdAt":"2024-05-14 20:05:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4421269/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4421269/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59399306,"identity":"ed11dadf-b62e-4d56-a6d1-470d881c584c","added_by":"auto","created_at":"2024-07-01 09:50:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1097176,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of experimental workflow used in this study. Representative gel images from Coomassie-stained 2DE and DIGE experiments are shown in the figure. Venn diagram depicts the common differentially regulated proteins identified from the 2DE and DIGE gels using MALDI-TOF/TOF analysis\u003c/p\u003e","description":"","filename":"Figure1..png","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/dcece965a38b5b730ee78c47.png"},{"id":59398833,"identity":"7ee3e899-831a-461a-a1a9-b7a49f799020","added_by":"auto","created_at":"2024-07-01 09:42:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1623363,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of DIGE gels used for comparative analysis. The pooled protein extracts from GDM, T1DM, and control samples were labeled with Cy3, Cy5, and Cy2, respectively. The superimposed image was the product of superimposing all three Cye dye images; the images were pseudo-colored\u003c/p\u003e","description":"","filename":"Figure2..png","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/4f8c20f6ddc2a98331e8c11f.png"},{"id":59399307,"identity":"50ec5642-3404-4b63-b664-aa2fd0d38e3b","added_by":"auto","created_at":"2024-07-01 09:50:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4873746,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative gel images of protein distribution patterns of OAT, PT, and SMT samples. The master gels were automatically created using the PD-Quest software. Master gel images were used for the excision of differentially regulated protein spots\u003c/p\u003e","description":"","filename":"Figure3.Final.png","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/a1c8c9c70f8d5a785a3e1557.png"},{"id":59398834,"identity":"c4780afe-db55-40c3-8def-54563198915f","added_by":"auto","created_at":"2024-07-01 09:42:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1784923,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative close-up spot images for some differentially regulated protein spots in tissues. The arrows indicate differentially regulated protein spots in different gels. Bar graphs show the protein spot intensities in the experimental groups\u003c/p\u003e","description":"","filename":"Figure4.Clouseupspots.png","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/6628658c031ff6048dded831.png"},{"id":59399305,"identity":"6395fb01-8953-429f-869d-17c127e699eb","added_by":"auto","created_at":"2024-07-01 09:50:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":970823,"visible":true,"origin":"","legend":"\u003cp\u003eDemonstration of spot intensity changes observed in 2DE gels by western blotting. β-Actin was used as an internal control. Bar graphs show the protein spot intensities in the experimental groups\u003c/p\u003e","description":"","filename":"Figure5.WBs.png","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/15c672daa811001a9399aa0b.png"},{"id":59398830,"identity":"d2ba7574-5af0-4613-a8b8-d182834e420f","added_by":"auto","created_at":"2024-07-01 09:42:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":487532,"visible":true,"origin":"","legend":"\u003cp\u003eThe Venny graph demonstrates that carbonic anhydrase 1 (CA1) emerged as the singular protein that was consistently shared among all experimental groups\u003c/p\u003e","description":"","filename":"Figure6.Venny.png","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/e39f5c792bf75afdaac36e64.png"},{"id":63302232,"identity":"11326a14-6ed7-484a-bcf1-4b850d05404e","added_by":"auto","created_at":"2024-08-26 16:27:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13908969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4421269/v1/39797272-f951-459b-82c9-eb6fe77e95f6.pdf"}],"financialInterests":"","formattedTitle":"The Differentiation of Proteome Analysis of Omental Adipose Tissue, Placenta and Skeletal Muscle in between Pregnant Women with Gestational Diabetes and Type 1 Diabetes Mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM), which is associated with genetic and environmental risk factors, is defined as any level of glucose intolerance diagnosed during pregnancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The estimated prevalence of GDM ranges from 1\u0026ndash;14% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Insulin resistance is the main pathophysiological mechanism responsible for GDM. GDM develops when pancreatic β-cells cannot function properly and are unable to handle impaired glucose metabolism owing to severe insulin resistance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Under normal conditions, a metabolic shift from lipid synthesis to lipid oxidation occurs during pregnancy to preserve the adequate supply of glucose to the growing fetus [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In GDM patients, however, elevated plasma glucose levels prevent the shift of metabolism towards lipid oxidation and thus inhibit the production of glucose [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThree body parts are well established to be associated with GDM. The first is skeletal muscle, where lipid oxidation occurs. The second is adipose tissue, which expands in weight, diameter, and thickness during pregnancy. The expansion of adipose tissue can trigger the secretion of pro-inflammatory cytokines and may contribute to the development of GDM [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The placenta is the third part of the body that becomes heavier and larger in GDM in order to maintain its efficiency to provide the increased nutritional requirements of the fetus [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Evidence shows that adipose tissue-derived exosomes from pregnant women with GDM can impair placental glucose metabolism [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral proteomic studies analyzing maternal plasma or urine have been performed to predict the development of GDM [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, changes in the proteomic profiles of the placenta, adipose tissue, and skeletal muscle have been studied to elucidate the pathogenesis and other consequences of hyperglycemia in GDM [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, there are no data in the literature on proteomic analysis of tissues, which was the focus of this study, from pregnant women with T1DM. However, it is not surprising to observe the effect of increased plasma glucose levels in each tissue of the T1DM group, rather than a change in protein expression that contributes to the development of GDM.\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to differentiate the proteins between three different tissues from GDM and T1DM patients. Therefore, changes in the proteomic profiles of omental adipose (OAT), placental (PT), and skeletal muscle (SMT) tissues collected during delivery from GDM and T1DM patients and healthy controls were investigated.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTissue Sample Collection\u003c/h2\u003e \u003cp\u003ePregnant women diagnosed with GDM (n\u0026thinsp;=\u0026thinsp;11) or T1DM (n\u0026thinsp;=\u0026thinsp;11) and healthy pregnant controls (n\u0026thinsp;=\u0026thinsp;11) receiving care at our clinic were included in this study. They were monitored in the Department of Endocrinology, and only patients diagnosed with GDM or T1DM based on blood tests were included in the study. Informed consent was obtained from each patient and the study was approved by the Institutional Ethics Committee. Placental, omental, and smooth muscle tissues were collected from both GDM and T1DM patients and from healthy controls during cesarean section. Five punch biopsies from various areas of each tissue were randomly pooled immediately after cesarean section and washed in cold PBS to eliminate any contaminating blood. The tissues were then frozen in liquid nitrogen and stored at -80\u0026deg;C until use [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The clinical and biochemical properties of the patients and controls are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the study groups. Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.E.M..\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 1 DM\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep- value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal age (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal BMI at 12 weeks (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal BMI at delivery (kg/m2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational age at birth (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb, c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFetal birth weight (g)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3234\u0026thinsp;\u0026plusmn;\u0026thinsp;395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3140\u0026thinsp;\u0026plusmn;\u0026thinsp;407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3160\u0026thinsp;\u0026plusmn;\u0026thinsp;520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFetal gender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 F; 5 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 F; 5 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 F; 6 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGravida\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal OGTT at 28 weeks of gestation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting plasma OGTT (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-h plasma OGTT (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2-h plasma OGTT (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal OGTT at 6 weeks of postpartum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting plasma OGTT (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-h plasma OGTT (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2-h plasma OGTT (mg/dl)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemoglobin A1C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3, a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNS, not significant; ND, not done; OGTT, oral glucose tolerance test; F, female; M, male.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea, statistically significant between groups 1 and 2; b, statistically significant between groups 1 and 3; c, statistically significant between groups 2 and 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eProtein extraction\u003c/h2\u003e \u003cp\u003eTissue samples from patients were diced on ice in a sterile glass culture plate and washed three times with an ample amount of ice-cold washing buffer (10 mM Tris-HCl, pH 7.0, 250 mM sucrose) to remove excess blood. After 10 min of centrifugation at 4\u0026deg;C at 2000 \u0026times;g, excess wash buffer was decanted, and 250 \u0026micro;l of buffer containing 7 M urea, 2 M thiourea, 4% CHAPS, 30 mM Tris pH 8.5, and 1x protease inhibitor cocktail was added to each tissue pellet. The tissue pellets were lysed using 0.5 mm stainless steel beads with the help of a homogenizer (Next Advance, Averill Park, New York, USA) at +\u0026thinsp;4\u0026ordm;C. The supernatant containing the soluble protein extract was obtained by centrifugation at 20,000 \u003cem\u003eg\u003c/em\u003e for 30 min at 4\u0026deg;C. Protein concentration was determined using the Bradford assay with a BSA standard (Bio-Rad, Hercules, California, USA). The soluble protein-containing supernatants were stored in Lo-bind tubes (Eppendorf, Hamburg, Germany) at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of pooled samples\u003c/h2\u003e \u003cp\u003eEqual amounts of protein from each sample were combined in a single tube, and the protein concentration of the pooled samples was re-measured. The protein concentration estimated by measurement was compared with the calculated protein concentration to validate that the samples were correctly pooled. Further validation was achieved using SDS-PAGE, followed by visual examination of the protein profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMinimal protein labeling and the difference gel electrophoresis (DIGE)\u003c/h2\u003e \u003cp\u003eFor DIGE experiments, protein samples were prepared in minimal DIGE lysis buffer (7 M urea, 2 M thiourea, 4% CHAPS w/v, 30mM Tris-HCl, pH 8.5), and equal amounts of proteins were pooled for each group. DIGE-specific Cy2, Cy3, and Cy5 dyes (Life Tech, USA) were used for labeling of 50 \u0026micro;g pooled protein samples in each experiment. A separate DIGE experiment was designed for each tissue type (OAT, PT, and SMT), and the tissues were marked with Cye dyes according to the experimental group they belonged to. Protein pools obtained from the OAT, PT, and SMT tissues were labeled with Cy3 for the GDM group, Cy5 for the T1DM group, and Cy2 for the control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTwo-dimensional gel electrophoresis (2DE)\u003c/h2\u003e \u003cp\u003eFor first-dimensional separation \u003cem\u003evia\u003c/em\u003e isoelectric focusing (IEF), 750 \u0026micro;g of each pooled protein sample was loaded onto immobilized pH gradient strips (IPG) (17 cm, pH 3\u0026ndash;10) via passive rehydration at 20\u0026deg;C for 16 h. Isoelectric focusing was performed using a Protean isoelectric focusing cell (Bio-Rad, Hercules, California, USA). Following the focusing step, the IPG strips were loaded directly onto 1 mm-thick 12% sodium dodecyl sulfate-polyacrylamide gels. After electrophoresis, the gels were stained with colloidal Coomassie Blue (Bio-Rad, Hercules, California, USA). Two separate gels were run for each pooled group to minimize experimental variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImage analysis of 2DE and DIGE gels\u003c/h2\u003e \u003cp\u003eConventional 2DE gels were visualized with VersaDoc MP4000 (Bio-Rad, USA), and PDQuest 2DE Analysis Software (Bio-Rad, USA) was used for spot intensity calibration, spot detection, and background subtraction. DIGE gels were also visualized with VersaDoc MP4000 (Bio-Rad, Hercules, California, USA) using three different light sources. The number of spots was normalized to the total valid spot intensity by using a linear regression model. Paired Student\u0026rsquo;s t-tests were used to assess differences in the average protein abundance between the gels. 2D gel image comparison and protein spot intensities with more than a two-fold significant change (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in a consistently increased or decreased pattern were considered differentially expressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eInter-experimental comparison between 2DE and DIGE experiments\u003c/h2\u003e \u003cp\u003eTo perform an inter-experimental comparison between the conventional 2DE and DIGE experiments, \u0026ldquo;\u003cem\u003ethe compare experiment wizard\u003c/em\u003e\u0026rdquo; implemented in PD-Quest Advance software was used. Two master gels created during analysis of the gel images from each independent experimental study were compared, and overlapping differentially regulated protein spots were cut and identified by MALDI-TOF/TOF analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein identification by MALDI-TOF/TOF\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eProtein identification experiments were performed at the Kocaeli University Medical Biology Proteomics Laboratory using the ABSCIEX MALDI-TOF/TOF 5800 system (Applied Biosystems, Framingham, MA, USA). Spots of interest were subjected to in-gel tryptic digestion using an in-gel digestion kit, following the manufacturer\u0026rsquo;s protocol (Pierce, USA). Before deposition onto a MALDI plate, all samples were desalted with 10 \u0026micro;l ZipTipC18 (Millipore, USA). The TOF spectra were recorded in the positive ion reflector mode with a mass range from 400 to 2000 Da. The spectra were calibrated using the trypsin autodigestion ion peak \u003cem\u003em/z\u003c/em\u003e (842.510 and 2211.1046) as internal standards. Ten of the strongest peaks in the TOF spectra per sample were chosen for MS/MS analysis. All of the PMFs were searched in the MASCOT version 2.5 (Matrix Science) using the streamline software ProteinPilot (ABSCIEX, USA), with the following criteria: SWISSPROT database; species restriction to \u003cem\u003eH. sapiens;\u003c/em\u003e enzyme of trypsin; at least ten independent peptides matched; at most one missed cleavage site; MS tolerance set to \u0026plusmn;\u0026thinsp;50 ppm and MS/MS tolerance set to \u0026plusmn;\u0026thinsp;0.2 Da; fixed modification being carbamidomethyl (Cys) and variable modification being oxidation (Met); peptide charge of +\u0026thinsp;1 and monoisotopic. Only significant hits, as defined by the MASCOT probability analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), were accepted. A protein-protein interaction (PPI) network of the identified proteins was constructed using the online analysis tool STRING v10.0.\u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation of differentially regulated proteins using western blot analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe identified proteins were validated by western blot analysis using specific primary antibodies against carbonic anhydrase (1:500, Santa Cruz, sc-393490), superoxide dismutase (1 : 1000, Santa Cruz, sc-271014), glyceraldehyde-3-phosphate dehydrogenase (1:1,000, Santa Cruz, sc-47724), alpha enolase (1 : 5000, Santa Cruz, sc-101513), creatine kinase-M (1 : 5000, Santa Cruz, sc-69848), and β-actin (1:2,500, Santa Cruz, sc-8432). β-Actin was used as an internal control to ensure equal protein loading. The membranes were visualized with an ECL detection system (Bio-Rad, USA) using X-ray films and a VersaDoc imaging system (Bio-Rad, USA). Band intensities were quantified using the Quantity One 1D image analysis software (Bio-Rad, USA). Western blot analyses were performed only once for each antibody to confirm the changes in the protein spot intensities observed in the 2D gels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical significance of image analysis was determined by the Student\u0026rsquo;s t-test (statistical level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFor proteomic analysis, two complementary approaches, DIGE and 2DE, were used. The experimental workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. DIGE analysis revealed the presence of 230, 217, and 220 well-separated protein spots on the gels produced from the OAT, PT, and SMT protein pools, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the OAT tissues, we found that 21 protein spots were differentially regulated in the GDM and T1DM groups compared with the control. Using a similar analysis, we detected 17 protein spots in PT tissues and 18 protein spots in SMT. A similar comparative protein spot analysis was performed using 2DE gels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Each experimental group displayed spot distribution patterns with high spot matching rates (\u0026gt;95%). The average number of protein spots for the OAT, PT, and SMT groups was 420\u0026thinsp;\u0026plusmn;\u0026thinsp;17 (OAT), 407\u0026thinsp;\u0026plusmn;\u0026thinsp;20(PT), and 412\u0026thinsp;\u0026plusmn;\u0026thinsp;14 (SMT), respectively. For the OAT tissues, when we performed comparative 2DE gel analysis, we found that the shared 27 protein spots were differentially regulated in both GDM vs. control and T1DM vs. control. Using a similar analysis, we detected 22 protein spots in PT tissues and 19 protein spots in SM tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor complementation purposes, differentially regulated protein spots from the DIGE and 2DE gels were compared. Protein spots that were common to both experiments were considered more reliable than those that were differentially regulated only in one of the experimental approaches. For the OAT tissues, we found that 18 proteins were shared in both the DIGE and 2DE experiments. Using a similar method, we detected 15 shared proteins in PT tissues and 12 shared proteins in SMT tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ratios of regulation relative to the control for the proteins shared within each tissue, along with the Mass Spectrometry (MS) identification parameters, are described in detail in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ratios of regulation relative to the control for the proteins shared within each tissue\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC no.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein Acc.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMw (kd)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS/MS Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMatches\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003epI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSeq.\u0026nbsp;Cov. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProtein Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eGDM/Control\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eT1DM/Control\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e \u003cp\u003e\u003cb\u003eOmental adipose tissue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP27797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCALR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,00E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCalreticulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPOA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,00E-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eApolipoprotein A-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e7,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePDIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,00E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProtein disulfide-isomerase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP09525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANXA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,40E-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAnnexin A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFIBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,10E-47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFibrinogen beta chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK2C8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,60E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKeratin, type II cytoskeletal 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9UMS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePRP19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePre-mRNA-processing factor 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e6,41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ8N7Z2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGG6L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,60E\u0026thinsp;+\u0026thinsp;02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGolgin subfamily A member 6-like protein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBeta-2-glycoprotein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAL1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,10E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAldehyde dehydrogenase 1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,00E-36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCarbonic anhydrase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCRYAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,10E-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAlpha-crystallin B chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSODM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,30E-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSuperoxide dismutase [Mn], mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP68871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,00E-43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHemoglobin subunit beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP01834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIGKC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,10E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIg kappa chain C region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG3P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,10E-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGlyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP07355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANXA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,60E-51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAnnexin A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFIBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,10E-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFibrinogen alpha chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e\u003cb\u003ePlecental tissue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEndoplasmin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP27797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCALR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,10E-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCalreticulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGLU2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,40E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGlucosidase 2 subunit beta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP21980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,30E-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eProtein-glutamine gamma-glutamyltransferase 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,00E-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGuanine nucleotide-binding protein G(i) subunit alpha-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP14061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDHB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,00E-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEstradiol 17-beta-dehydrogenase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSPB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,40E-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHeat shock protein beta-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e10,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKU86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,20E-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eATP-dependent DNA helicase 2 subunit 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP30048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePRDX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,10E-21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eThioredoxin-dependent peroxide reductase, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,60E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCarbonic anhydrase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP06733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,00E-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAlpha-enolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,00E-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLamin-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSODM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,30E-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSuperoxide dismutase [Mn], mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ07955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSFRS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,10E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSplicing factor, arginine/serine-rich 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP05108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCP11A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,60E-57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCholesterol side-chain cleavage enzyme, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eSmooth muscle tissue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP04792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSPB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,00E-23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHeat shock protein beta-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP02675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFIBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,20E-47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFibrinogen beta chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP12883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e222959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,00E-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMyosin-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP35609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,00E-50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAlpha-actinin-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO14558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHSPB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,20E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHeat shock protein beta-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2,64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP06733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,60E-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAlpha-enolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP00915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,10E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCarbonic anhydrase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP45378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTNNT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8,10E-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTroponin T, fast skeletal muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP06732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKCRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,00E-41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCreatine kinase M-type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP11217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePYGM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,60E-70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eGlycogen phosphorylase, muscle form\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9NP98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMYOZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,20E-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMyozenin-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP13929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,10E-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBeta-enolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e\u0026darr;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo verify the regulatory trends, western blot analyses were performed for some of the selected proteins, namely mitochondrial superoxide dismutase (SODM) and Glyceraldehyde-3-phosphate dehydrogenase (G3P) in the omental tissue, alpha-enolase (ENOA) and SODM in the placental tissue, and ENOA and creatine kinase M-type (KCRM) in the smooth muscle tissue. For the selected proteins, close-up images of their corresponding spots were produced to confirm the regulatory trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Western blotting was performed and analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The regulation trends observed in the 2DE and DIGE experiments matched those observed in the western blots.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the investigation of regulatory patterns across experimental groups (GDM versus control or T1DM versus control), the comparative analysis of differentially regulated proteins in omental adipose, placental, and smooth muscle tissues revealed intriguing insights. Carbonic anhydrase 1 (CA1) emerged as the singular protein that was consistently shared among all experimental groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Analysis of close-up images of CA1 in 2DE gels showed that the protein was downregulated in all T1DM tissue samples compared with controls. In GDM patients, however, CA1 was upregulated in OAT and PT samples, while it was downregulated in SMT samples compared to controls.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we aimed to investigate the differences in protein expression associated with GDM and T1DM across three distinct tissue samples: the omentum, placenta, and skeletal muscle. Our primary objective was to identify proteins that exhibit inverse expression patterns in at least two tissue samples from each group, with the intention of distinguishing between proteomic alterations in GDM and T1DM.\u003c/p\u003e \u003cp\u003eWhen comparing the three distinct tissue types in patients with GDM and T1DM and their respective control groups, we observed significant differences in the protein expression profiles. Specifically, six proteins (carbonic anhydrase 1, alpha-enolase, calreticulin, fibrinogen beta chain, mitochondrial superoxide dismutase, and heat shock protein beta-1) exhibited distinctive expression patterns in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, among these six proteins, only two demonstrated contrasting trends in the two different tissue samples within the GDM and T1DM groups. An inverse expression pattern of CA1 was consistently observed in both OAT and PT, whereas the differentiation pattern of ENOA was consistent in PT and SMT. These two proteins were upregulated in two distinct samples from GDM patients, but downregulated in the corresponding samples from T1DM patients.\u003c/p\u003e \u003cp\u003eCarbonic anhydrase (CA) is a zinc metalloenzyme that catalyzes the conversion of CO2 to HCO3\u0026thinsp;\u0026minus;\u0026thinsp;and H\u0026thinsp;+\u0026thinsp;in various organisms. In mammals, 14 different isoforms of this zinc metalloenzyme have been identified, and they are found in various cellular compartments, including the cytosol, mitochondria, and cell membrane [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Among the cytosolic isoforms, CA1 plays a significant role in multiple physiological processes, such as the regulation of acid-base balance, ion transport, and gas exchange in numerous organs and tissues. Disruptions in the function of this enzyme have been linked to the pathophysiology of several diseases, including insulin resistance and diabetes mellitus. In a case-control study involving 86 participants; it was observed that CA activity was elevated in individuals with insulin resistance when compared to the control group [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. One of CA's crucial roles of CA is to provide HCO3- as a substrate for the initial step in hepatic gluconeogenesis, which was a key finding in this study [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, Kondo \u003cem\u003eet al\u003c/em\u003e. reported an increased level of CA1 in patients with diabetes mellitus, indicating its potential significance in this disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. From a different perspective, various clinical trials have consistently reported that elevated plasma glucose levels are associated with a decrease in erythrocyte carbonic anhydrase (CA) activity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This reduction in enzymatic activity is attributed to the glycosylation of CA, a process initiated by increased exposure of erythrocytes to elevated glucose levels in our current study. We observed upregulation of CA1 expression in OAT and PT samples from individuals with GDM, while downregulation of CA1 was noted in the same tissues from patients with T1DM. Considering the previously discussed mechanisms, our findings suggest that OAT and PT may play pivotal roles in the development of impaired glucose metabolism in pregnant women, potentially reflecting the acute impact of hyperglycemia. Conversely, the persistent effects of hyperglycemia in T1DM were evident in these two tissues. Furthermore, the consistent downregulation of CA1 in SMTs from both GDM and T1DM groups provides compelling evidence that smooth muscle is a shared tissue in which the effects of both acute and chronic hyperglycemia manifest.\u003c/p\u003e \u003cp\u003eAlpha-enolase (ENOA) is a widely distributed metalloenzyme found in the cytoplasm, nucleus, and cell surface [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Among its various functions, ENOA plays a crucial role in the glycolytic pathway, catalyzing the conversion of 2-phosphoglyceric acid to phosphoenolpyruvic acid, particularly in response to stress. Several proteomic studies have reported that ENOA is differentially expressed before and after stress exposure [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, Lu \u003cem\u003eet al\u003c/em\u003e. observed significant upregulation of ENOA expression in the liver and heart of diabetic rats, which was interpreted as a protective response to counteract oxidative and nitrative stress associated with diabetes, aimed at preventing tissue damage [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In another study, the authors reported an increase in ENOA expression in the poor glycemic control group [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These findings underscore the significance of ENOA in the context of stress responses and its potential implications for diabetes-related complications.\u003c/p\u003e \u003cp\u003eConsistent with the existing literature, our study revealed upregulation of ENOA expression in the PT and SMT of the GDM group. This finding underlines the likelihood that impaired glucose metabolism in pregnant women with GDM may influence placental tissue as well as SMT. Notably, we observed a downregulation of ENOA in the placental and SMT samples from the Type 1 Diabetes Mellitus (T1DM) group, which suggests that chronic hyperglycemia may compromise acute compensatory mechanisms, such as the upregulation of ENOA, in patients with T1DM.\u003c/p\u003e \u003cp\u003eA limitation of our study pertains to the comparison of protein results derived from distinct tissue types. To address this concern, we adopted an approach in which, rather than directly comparing the 2-dimensional electrophoresis (2DE) gel profiles between groups, we focused on comparing the identified proteins within each group to evaluate variations across different groups. This strategy allowed us to mitigate the challenges associated with inter-tissue differences, while facilitating a more precise assessment of protein variations between groups.\u003c/p\u003e \u003cp\u003eIn conclusion, our study identified 23 proteins that exhibited common alterations and 18 proteins that displayed inverse changes in OAT, PT, and SMT among pregnant women with either GDM or T1DM when compared to a control group. Among these 18 differentially expressed proteins, CA1 and ENOA differed from the others in that they were upregulated in GDM and downregulated in T1DM in at least two different tissues compared to the controls. CA1 and ENOA exhibited up-regulation in GDM and down-regulation in T1DM compared to controls in the AOT - PT samples, and PT - SMT samples, respectively. To the best of our knowledge, this study marks an inaugural attempt to distinguish proteomic profile changes across diverse tissues in pregnant women diagnosed with GDM and T1DM when compared to healthy controls. Our results might help shed light on proteomic alterations that could potentially elucidate the underlying pathophysiological mechanisms contributing to the development of GDM, as well as the repercussions of impaired glucose metabolism resulting from both short-term (newly developed GDM) and long-term (T1DM) hyperglycemia during pregnancy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 114S412. The funding body had no role in the design or execution of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e: This study was approved by the Ethics Committee of Kocaeli University (approval number: KOU KAEK 2013/79).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: The authors report no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e: Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eThe study conception and experimental design were performed by MK, GA, and ZC. Tissue collection with surgery and storage was performed by AYC and ZC. Sample preparation and protein isolation were performed by EG, MK, and GA. The experiments were performed by EG, GA, and MK. Bioinformatics analysis was performed by GA and MK. Clinical evaluation of the data was performed by ZC and YC. The first draft of the manuscript was written by GA and EG. Writing, review, and editing of the draft were performed by GA and MK. All authors commented on the previous versions of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAssociation AD (2009) Diagnosis and classification of diabetes mellitus. Diabetes Care 32:S62\u0026ndash;S67\u003c/li\u003e\n\u003cli\u003eHaghvirdizadeh P, Mohamed Z, Abdullah NA, et al (2015) KCNJ11: Genetic Polymorphisms and Risk of Diabetes Mellitus. J Diabetes Res 2015:. https://doi.org/10.1155/2015/908152\u003c/li\u003e\n\u003cli\u003eDabelea D, Snell-Bergeon JK, Hartsfield CL, et al (2005) Increasing prevalence of gestational diabetes mellitus (GDM) over time and by birth cohort: Kaiser Permanente of Colorado GDM screening program. Diabetes Care 28:579\u0026ndash;584. https://doi.org/10.2337/diacare.28.3.579\u003c/li\u003e\n\u003cli\u003eAssociation AD (2003) Gestational diabetes mellitus. Diabetes Care 26:s103\u0026ndash;s105\u003c/li\u003e\n\u003cli\u003eErnst S, Demirci C, Valle S, et al (2011) Mechanisms in the adaptation of maternal \u0026beta;-cells during pregnancy. Diabetes Manag 1:239\u0026ndash;248. https://doi.org/10.2217/dmt.10.24\u003c/li\u003e\n\u003cli\u003eCatalano PM, Nizielski SE, Shao J, et al (2002) Downregulated IRS-1 and PPAR\u0026gamma; in obese women with gestational diabetes: Relationship to FFA during pregnancy. 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Metabolism 63:250\u0026ndash;262. https://doi.org/10.1016/j.metabol.2013.10.001\u003c/li\u003e\n\u003cli\u003eRojas-Rodriguez R, Lifshitz LM, Bellve KD, et al (2015) Human adipose tissue expansion in pregnancy is impaired in gestational diabetes mellitus. Diabetologia 58:2106\u0026ndash;2114. https://doi.org/10.1007/s00125-015-3662-0\u003c/li\u003e\n\u003cli\u003eSogabe M, Okahisa T, Hibino S, Yamanoi A (2012) Usefulness of differentiating metabolic syndrome into visceral fat type and subcutaneous fat type using ultrasonography in Japanese males. J Gastroenterol 47:293\u0026ndash;299. https://doi.org/10.1007/s00535-011-0489-4\u003c/li\u003e\n\u003cli\u003eDonohoe CL, Doyle SL, Reynolds J V. (2011) Visceral adiposity, insulin resistance and cancer risk. Diabetol. Metab. Syndr. 3\u003c/li\u003e\n\u003cli\u003eLiu B, Xu Y, Voss C, et al (2012) Altered Protein Expression in Gestational Diabetes Mellitus Placentas Provides Insight into Insulin Resistance and Coagulation/Fibrinolysis Pathways. PLoS One 7:1\u0026ndash;12. https://doi.org/10.1371/journal.pone.0044701\u003c/li\u003e\n\u003cli\u003eDaskalakis G, Marinopoulos S, Krielesi V, et al (2008) Placental pathology in women with gestational diabetes. Acta Obstet Gynecol Scand 87:403\u0026ndash;407. https://doi.org/10.1080/00016340801908783\u003c/li\u003e\n\u003cli\u003eDesoye G, Hauguel-De Mouzon S (2007) The human placenta in gestational diabetes mellitus: The insulin and cytokine network. Diabetes Care 30:S120\u0026ndash;S126. https://doi.org/10.2337/dc07-s203\u003c/li\u003e\n\u003cli\u003eJayabalan N, Lai A, Ormazabal V, et al (2019) Adipose Tissue Exosomal Proteomic Profile Reveals a Role on Placenta Glucose Metabolism in Gestational Diabetes Mellitus. J Clin Endocrinol Metab 104:1735\u0026ndash;1752. https://doi.org/10.1210/jc.2018-01599\u003c/li\u003e\n\u003cli\u003eKim SM, Park JS, Norwitz ER, et al (2012) Identification of proteomic biomarkers in maternal plasma in the early second trimester that predict the subsequent development of gestational diabetes. Reprod Sci 19:202\u0026ndash;209. https://doi.org/10.1177/1933719111417889\u003c/li\u003e\n\u003cli\u003eMavreli 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 (Brooklyn) 34:517\u0026ndash;525. https://doi.org/10.21873/invivo.11803\u003c/li\u003e\n\u003cli\u003eZhao D, Shen L, Wei Y, et al (2017) Identification of candidate biomarkers for the prediction of gestational diabetes mellitus in the early stages of pregnancy using iTRAQ quantitative proteomics. Proteomics - Clin Appl 11:1\u0026ndash;32. https://doi.org/10.1002/prca.201600152\u003c/li\u003e\n\u003cli\u003eRavnsborg T, Svaneklink S, Andersen LLT, et al (2019) First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus. PLoS One 14:1\u0026ndash;13. https://doi.org/10.1371/journal.pone.0214457\u003c/li\u003e\n\u003cli\u003eGuo Y, Han Z, Guo L, et al (2018) Identification of urinary biomarkers for the prediction of gestational diabetes mellitus in early second trimester of young gravidae based on iTRAQ quantitative proteomics. Endocr J 65:727\u0026ndash;735. https://doi.org/10.1507/endocrj.EJ17-0471\u003c/li\u003e\n\u003cli\u003eRamachandrarao SP, Hamlin AA, Awdishu L, et al (2016) Proteomic analyses of Urine Exosomes reveal New Biomarkers of Diabetes in Pregnancy. Madridge J Diabetes 1:11\u0026ndash;22. https://doi.org/10.18689/mjd-1000103\u003c/li\u003e\n\u003cli\u003eLiao Y, Xu GF, Jiang Y, et al (2018) Comparative proteomic analysis of maternal peripheral plasma and umbilical venous plasma from normal and gestational diabetes mellitus pregnancies. Med (United States) 97:. https://doi.org/10.1097/MD.0000000000012232\u003c/li\u003e\n\u003cli\u003eLiu X, Sun J, Wen X, et al (2020) Proteome profiling of gestational diabetes mellitus at 16-18 weeks revealed by LC-MS/MS. J Clin Lab Anal 34:1\u0026ndash;10. https://doi.org/10.1002/jcla.23424\u003c/li\u003e\n\u003cli\u003eOliva K, Barker G, Rice GE, et al (2013) 2D-DIGE to identify proteins associated with gestational diabetes in omental adipose tissue. J Endocrinol 218:165\u0026ndash;178. https://doi.org/10.1530/JOE-13-0010\u003c/li\u003e\n\u003cli\u003eMa Y, Gao J, Yin J, et al (2016) Identification of a Novel Function of Adipocyte Plasma Membrane-Associated Protein (APMAP) in Gestational Diabetes Mellitus by Proteomic Analysis of Omental Adipose Tissue\u003c/li\u003e\n\u003cli\u003eHerrera-Van Oostdam AS, Salgado-Bustamante M, L\u0026oacute;pez JA, et al (2019) Placental exosomes viewed from an \u0026ldquo;omics\u0026rdquo; perspective: Implications for gestational diabetes biomarkers identification. Biomark Med 13:675\u0026ndash;684. https://doi.org/10.2217/bmm-2018-0468\u003c/li\u003e\n\u003cli\u003eLapolla A, Porcu S, Roverso M, et al (2013) A preliminary investigation on placenta protein profile reveals only modest c hanges in well controlled gestational diabetes mellitus. Eur J Mass Spectrom 19:211\u0026ndash;223. https://doi.org/10.1255/ejms.1225\u003c/li\u003e\n\u003cli\u003eAssi E, D\u0026rsquo;Addio F, Mand\u0026ograve; C, et al (2020) Placental proteome abnormalities in women with gestational diabetes and large-for-gestational-age newborns. BMJ Open Diabetes Res Care 8:1\u0026ndash;8. https://doi.org/10.1136/bmjdrc-2020-001586\u003c/li\u003e\n\u003cli\u003eBoyle KE, Hwang H, Janssen RC, et al (2014) Gestational diabetes is characterized by reduced mitochondrial protein expression and altered calcium signaling proteins in skeletal muscle. PLoS One 9:. https://doi.org/10.1371/journal.pone.0106872\u003c/li\u003e\n\u003cli\u003eGharesi-Fard B, Zolghadri J, Kamali-Sarvestani E (2010) Proteome Differences of Placenta Between Pre-Eclampsia and Normal Pregnancy. Placenta 31:121\u0026ndash;125. https://doi.org/10.1016/j.placenta.2009.11.004\u003c/li\u003e\n\u003cli\u003eMori K, Ogawa Y, Ebihara K, et al (1999) Isolation and characterization of CA XIV, a novel membrane-bound carbonic anhydrase from mouse kidney. J Biol Chem 274:15701\u0026ndash;15705. https://doi.org/10.1074/jbc.274.22.15701\u003c/li\u003e\n\u003cli\u003eBiswas UK, Kumar A (2012) Study on the changes of carbonic anhydrase activity in insulin resistance and the effect of methylglyoxal. J Pak Med Assoc 62:417\u0026ndash;421\u003c/li\u003e\n\u003cli\u003eIsmail IS (2018) The Role of Carbonic Anhydrase in Hepatic Glucose Production. Curr Diabetes Rev 14:108\u0026ndash;112. https://doi.org/10.2174/1573399812666161214122351\u003c/li\u003e\n\u003cli\u003eKondo T, Murakami K, Ohtsuka Y, et al (1987) Estimation and characterization of glycosylated carbonic anhydrase I in erythrocytes from patients with diabetes mellitus. Clin Chim Acta 166:227\u0026ndash;236. https://doi.org/10.1016/0009-8981(87)90425-6\u003c/li\u003e\n\u003cli\u003eAbel P, Wussow S, Bl\u0026uuml;cher H, et al (1997) Erythrocyte carbonic anhydrase activity in smokers and in diabetic patients. Exp Clin Endocrinol Diabetes 105:17\u0026ndash;19. https://doi.org/10.1055/s-0029-1211788\u003c/li\u003e\n\u003cli\u003ePancholi V, Fischetti VA (1998) \u0026Alpha;-Enolase, a Novel Strong Plasmin(Ogen) Binding Protein on the Surface of Pathogenic Streptococci. J Biol Chem 273:14503\u0026ndash;14515. https://doi.org/10.1074/jbc.273.23.14503\u003c/li\u003e\n\u003cli\u003eJi H, Wang J, Guo J, et al (2016) Progress in the biological function of alpha-enolase. Anim Nutr 2:12\u0026ndash;17. https://doi.org/10.1016/j.aninu.2016.02.005\u003c/li\u003e\n\u003cli\u003eLu N, Zhang Y, Li H, Gao Z (2010) Oxidative and nitrative modifications of \u0026alpha;-enolase in cardiac proteins from diabetic rats. Free Radic Biol Med 48:873\u0026ndash;881. https://doi.org/10.1016/j.freeradbiomed.2010.01.010\u003c/li\u003e\n\u003cli\u003eLu N, Li J, He Y, et al (2014) Nitrative modifications of \u0026alpha;-enolase in hepatic proteins from diabetic rats: The involvement of myeloperoxidase. Chem Biol Interact 220:12\u0026ndash;19. https://doi.org/10.1016/j.cbi.2014.05.021\u003c/li\u003e\n\u003cli\u003eSoongsathitanon J, Umsa-Ard W, Thongboonkerd V (2019) Proteomic analysis of peripheral blood polymorphonuclear cells (PBMCs) reveals alteration of neutrophil extracellular trap (NET) components in uncontrolled diabetes. Mol Cell Biochem 461:1\u0026ndash;14. https://doi.org/10.1007/s11010-019-03583-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Proteomics, Gestational Diabetes, Type 1 Diabetes Mellitus, Pregnancy","lastPublishedDoi":"10.21203/rs.3.rs-4421269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4421269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe objective of this study was to investigate global changes in protein profiles within omental adipose (OAT), placental (PT), and smooth muscle tissues (SMT), with the aim of identifying potential triggering or affecting biomarkers in gestational (GDM) and type 1 diabetes (T1DM) by comparing them with the control group.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThree distinct tissue sources representing the two disease groups and the control group were collected and subjected to comparative proteomic analysis. This comprehensive approach was employed to elucidate the differentially regulated proteins among the groups. Western blot analysis was used to validate the observed changes at the protein level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 23 proteins exhibited common alterations, and 18 proteins displayed inverse changes in OAT, PT, and SMT among pregnant women with either GDM or T1DM compared to the control group. Among these 18 differentially expressed proteins, carbonic anhydrase 1 (CA1) and alpha-enolase (ENOA) differed from the others in that they were upregulated in GDM and downregulated in T1DM in the studied tissues compared with controls. Proteomic analyses highlighted alterations in the expression of CA1 protein, a shared feature across all groups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study marks an inaugural attempt to distinguish proteomic profile changes across diverse tissues in pregnant women diagnosed with GDM and T1DM when compared to healthy controls. The findings of this study could potentially elucidate the underlying pathophysiological mechanisms contributing to the development of GDM, as well as the repercussions of impaired glucose metabolism resulting from both short- and long-term hyperglycemia during pregnancy.\u003c/p\u003e","manuscriptTitle":"The Differentiation of Proteome Analysis of Omental Adipose Tissue, Placenta and Skeletal Muscle in between Pregnant Women with Gestational Diabetes and Type 1 Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 09:42:52","doi":"10.21203/rs.3.rs-4421269/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a75009c2-cd3f-4f68-b873-c055384af867","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-03T09:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-01 09:42:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4421269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4421269","identity":"rs-4421269","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}