Proteomic Profiling of Second and Third Trimester Pregnant Females Having Preterm Birth

Proteomic Profiling of Second and Third Trimester Pregnant Females Having Preterm Birth | 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 Article Proteomic Profiling of Second and Third Trimester Pregnant Females Having Preterm Birth Javeria Malik, Shaaf Ahmad, Muhammad Amir Iqbal, Tamseela Mumtaz, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4647134/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 Preterm birth is the most important obstetric complication, linked with serious health outcomes, huge financial loss and emotional stress to families. The study group included 200 females with second and third trimester of pregnancy. Out of total, 70 females delivered preterm. The samples containing proteins of interests were resolved by 2D-PAGE and after tryptic digestion LC-MS/MS analysis was done. The proteins were identified by Mascot software search engine. SameSpots software was employed to determine the raw volume of proteins. Statistical analysis was done by ANOVA. The presence of potential predictive biomarkers, Haptoglobin alpha chain and transthyretin in 2DE gels of both 2nd and 3rd trimester samples was confirmed. The results of our study showed slight increase in Haptoglobin alpha chain levels in very preterm and extremely preterm groups of 2nd trimester samples. While, in case of 3rd trimester samples the Haptoglobin levels showed significant increase in experimental groups. Considerable decreased expression of transthyretin level in extremely preterm group as compared to very preterm group and control subjects of 2nd trimester pregnancy was detected. While, in case of 3rd trimester samples a gradual significant decrease was observed in experimental groups. The significantly reduced level of transthyretin and increased level of Haptoglobin alpha chain as compared to healthy term pregnancy, will serve as major risk predictor of reduced gestational age. Biological sciences/Physiology Health sciences/Biomarkers Health sciences/Risk factors Health sciences/Signs and symptoms Preterm Birth Gestational Age Transthyretin Haptoglobin Alpha Chain Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Preterm birth is one of the most important obstetric complication, linked with serious health outcomes like newborn mortality and morbidity, huge financial loss to society and significant emotional stress to mothers and families [ 1 ]. Nearly, 15 million babies born preterm yearly and 28% of deaths in infancy are linked with prematurity [ 2 ]. The survival rates have significantly increased in recent past, because of improvements in neonatology and perinatology mainly for extremely premature infants (gestational age between 24–27 weeks) but unluckily the morbidity linked with survivors is still significant. About, one fifth to one quarter extremely premature newborns suffer some major disability such as blindness or deafness, impaired mental development, chronic lung diseases and cerebral palsy. late preterm newborns (gestational age between 32–36 weeks) also face the risk factors such as feeding difficulties, jaundice, respiratory distress syndrome and temperature instabilities [ 3 ]. Further, findings of earlier studies indicate a significant risk of stroke, cardiovascular mortality and hypertension in survivors of preterm birth [ 4 ]. The increasing number of complications compelled the obstetricians to diagnose effective therapeutic interventions that allow early disease prediction and prevention. In this regard the identification of constantly varying abundance of particular proteins may help to provide new diagnostic tools for early risk assessment of PTB [ 2 ]. Currently the diagnosis and prevention of preterm birth is established by identification of risk factor in second and third trimester of pregnancy [ 5 , 6 ]. PTB is a multifactorial condition, with multiple hereditary, infective, inflammatory and environmental factors possibly happening in one patient [ 7 ]. Pathophysiological paths linked with PTB are multifaceted and improperly understood. Hence, forecasting PTB has been a challenge since ages [ 8 ]. In this situation, the ideal screening model for PTB prediction is the assessment of serological biomarkers [ 7 ]. Our study primarily focused on role of Haptoglobin and transthyretin for prediction of preterm birth. Subjects only with spontaneous preterm delivery will be included in this investigation. The back bone of this investigation is to uncover some of the left over secret in the manifestation of PTB by evaluation of using ultra-sensitive and well-established top down proteomic method. Moreover, establishing new biomarkers in the biology of PTB will improve our approach in managing the patients in critical conditions. MATERIAL AND METHOD Sample Collection This study was approved by ethical review committee of Institute of Zoology, University of the Punjab. Pregnant females (n=260) in their second and third trimester were registered from public hospitals of Lahore (Sheikh Zaid Hospital, Jinnah Hospital and Lady Wellington Hospital). The purpose of the study was explained and informed consent clearly stating the study purpose was signed by the patients or their attendants. As the research involved human participants, it was performed in accordance with the relevant guidelines and regulations mentioned in the ‘Declaration of Helsinki’. The risk factors for preterm birth were examined by the medical history of the patients. Before sample collection each patient was asked about smoking status, age, previous medical history, parity, gravidity and complete history of miscarriages and abortion (table: 1). Systolic and diastolic blood pressure was recorded before sample collection. The subjects of age less than 18 and more than 40 were excluded from the study. Subjects with complications during pregnancy like extensive bleeding, multiple miscarriages, fetal congenital irregularities, ectopic pregnancy and any other confounding pathology were also excluded. At first, 350 pregnant females were registered in the study, fifty five females did not show any interest and withdrawn from the study. Thirty five females were not sure about their last menstrual period (LMP) date. Overall 260 females were followed till the end of their pregnancy. At their first visit, blood sample was taken and after delivery their total gestational age was noted. Out of 260, 150 females delivered preterm. First we separated them into second and third trimester samples based on time of sample collection. Further, we divided them into very preterm group (gestational age ≤ 32weeks) and extremely preterm group (gestational age ≤ 28 weeks). 100 females delivered at term with gestational age ≥ 37 weeks. Out of 150, 70 females were in the second trimester at the time of sampling and were considered as control I and 80 were in the third trimester at the time of sampling and considered as control II. Demographic features 2 nd trimester samples 3 rd trimester samples Controls (≥37 weeks) very preterm (≤ 32weeks) Extremely preterm ( ≤ 28 weeks ) P- VALUE Controls (≥37 weeks) Very preterm ( ≤ 28 weeks ) extremely preterm ( ≤ 28 weeks ) P- Value Age (years) 26.5±5.468 26.67±6.623 28.5±2.074 0.7568 28.71±6.448 24.14±5.757 28.43±3.309 0.2278 Parity 0 =50 % >0 =50 % 0 =0 % >0 =100 % 0 = 25% >0 = 75% - 0 =36 % >0 =54 % 0 =26 % >0 =74 % 0 =0 % >0 =100 % - Gravidity 1.250±0.50 2.429±1.512 2.500±1.00 0.2692 2.571±2.070 2.429±1.813 3.143±2.340 0.7969 Smoking No No No - No No No - Baby sex 50% Male 50%female 28%Male 72%female 25%Male 75%female NA 38%Male 62%female 42%Male 58%female 50%Male 50%female NA Table 1: The demographic features of the study group. Serum Separation Within 3 hours of phlebotomy the serum was separated. For serum separation, centrifugation of each blood sample was done for 10 minutes at 2500 rpm. Autoclaved cryovials were used for serum storage and labeled clearly with date and sample ID. For further processing, samples were stored at -80 ˚C. Protein Estimation For protein estimation, Bradford assay [9] was used. For quantification of protein, Bradford method is the highly specific and most accurate one. It is based on binding of coomassie blue G250 with protein molecules and gives maximum absorption at 595nm. Electrophoretic Analysis of Proteins After protein quantification, 2-dimensional gel electrophoresis was done that involves first dimensional analysis by Iso-Electric Focusing (IEF) and second dimensional analysis by SDS PAGE [10]. For iso-electric focusing, IPG strips of 17cm, linear, 3-10pH with catalog No. 163-2007 of BioRad Company were used. By using rehydration buffer samples of 250 µg protein/ 300 µL were prepared. To avoid evaporation, mineral oil was overlaid after loading the sample during rehydration. For 12hrs at 50V, rehydration was done and then focusing was performed at 20°C with 10000V. After the completion of iso-electric focusing, the IPG strip was equilibrated in SDS equilibration buffers 1 and 2, respectively in shaker. For second dimensional analysis, PROTEAN II XL cell (Bio-Rad) was used. On the inner plates of electrophoresis cell, 12% polyacrylamide gel was mounted. After shifting strip to the gel, it was over laid with 0.5% low melting point agarose. After that on the +ve end of the IPG strip, protein marker (15µL) was loaded. The electrophoretic cell was filled with running buffer. The gel was run firstly at 16mA and then at 35mA. After completion, the gel was transferred in fixative solution for overnight. Later on, staining of the gel was done in coomassie brilliant blue G250 as described by [11]. Finally, gel was destained in destaining solution. Protein Identification The proteins were identified by using peptide sequence databases through Mascot software search engine. Each sample was digested by trypsin to obtain peptides by set protocol [12]. After that Phase Nano-LC-MS/MS analysis was done and the peptides were separated by Shimadzu LC-20AD Nano nanoliter liquid chromatograph. Mass Spectrometry Detection was performed by ESI tandem mass spectrometer: TripleTOF 5600 (SCIEX, Framingham, MA, USA). Finally, the spectra obtained were analyzed to identify protein of interest using Mascot 2.3.02 (Fig. 1). 2D gel analysis by SameSpots After identification by LC-MS/MS and Mascot, Samespots software was used to determine the volume and area covered by each protein. Statistical analysis of data was done by using ANOVA Tukey’s Post Hoc analysis. RESULTS TRYPTIC DIGESTION AND LC-MS/MS RESULTS The presence of potential predictive biomarkers, Haptoglobin alpha chain and transthyretin in 2DE gels of both 2 nd and 3 rd trimester samples was confirmed by tryptic digestion and MALDITOF/LC-MS (Table. 2). Protein ID Protein identified Calculated MW (kDa)/pI Protein Score Matched peptide sequence Sequence coverage Percentage Unique spectra No. of unique peptides sp|P00738|HPT_ HUMAN Haptoglobin alpha chain 45.8/6.56 203 TEGDGVYTLNNEK 16.5 52 7 sp|P02766|TTHY_ HUMAN Transthyretin 15.9/5.58 224 VEIDTK 41 16 7 Table 2: The spots identification with LC-MS/MS. Identification of Haptoglobin Alpha Chain Spot no 12 was identified as Human Haptoglobin alpha chain. Mascot search result provided protein view as sp|P00738|HPT_HUMAN OX=9606 GN=HP PE=1 SV=1 with calculated MW (kDa/ pI) of protein was 45.8/6.56. The protein score was 158 with 7 unique peptides and 52 unique spectra. The search parameters included trypsin (as enzyme) which cut C-terminal side of KR unless next residue is P. Oxidation (M) was used as variable modification. The total percentage of coverage of resultant protein sequence was 16.5%. The total peptide sequence obtained. Bold letters represented the matched peptide sequence of Haptoglobin alpha chain. Identification of Transthyretin Spot no 13 was identified as Human transthyretin. Mascot search result provided protein view as protein id: sp|P02766|TTHY_HUMAN OX=9606 GN=TTR PE=1 SV=1 with calculated MW (kDa/ pI) of protein was 15.9/5.58. The protein score was 160 with 7 unique peptides and 16 unique spectra. The search parameters included trypsin (as enzyme) which cut C-terminal side of KR unless next residue is P. Oxidation (M) was used as variable modification. The total percentage of coverage of resultant protein sequence was 41% The total peptide sequence obtained. Bold letters represented the matched peptide sequence of transthyretin. 2DE GEL ANALYSIS For 2DE gel analysis SameSpots software and Swiss 2D page database were used (fig. 2). Table 2 elaborated the details of distinct protein identified and labeled in both very preterm and extremely preterm groups in fig. 3. Raw volume of both transthyretin and Haptoglobin alpha chain was calculated by ANOVA (Tukey’s Post Hoc analysis) (fig.4). The 3D expressions of transthyretin and Haptoglobin alpha chain were identified and both very preterm and extremely preterm groups were compared with controls with term delivery (fig. 5). DISCUSSION Pathophysiological paths linked with PTB are multifaceted and improperly understood. Hence, forecasting PTB has been a challenge since ages [ 8 ]. The pathogenesis of preterm birth is complex and multifactorial. Not many of the proposed biomarkers meet the standard to be considered clinically valuable for foreseeing preterm birth [ 13 ]. In present study, Haptoglobin alpha chain and transthyretin were selected for prediction of preterm birth in pregnant females in their second and third trimester of pregnancy. The results of our study showed slight increase in Haptoglobin alpha chain levels in very preterm and extremely preterm groups as compare to controls with term delivery of 2nd trimester samples. While in case of 3rd trimester samples the Haptoglobin levels of both experimental groups showed significant increase as compare to control group. Our study also demonstrated the significantly increased level of Haptoglobin alpha chain in healthy controls of 2nd trimester pregnancy as compared to control subjects of 3rd trimester pregnancy. Haptoglobin is an acute phase protein which shows marked elevation in case of acute or prolonged systemic inflammation [ 14 ]. Haptoglobin is functionally very important as it plays important role in protection of body against bacteriostatic activity, toxic free radicals and balancing between helper T cell type 1 and helper T cell type 2 immune responses. Past studies have reported that Haptoglobin has significant role in numerous diseases like tuberculosis, preeclampsia, diabetes and Preterm birth [ 15 ]. Basically Haptoglobin is synthesized by liver while in miner amounts it is synthesized by ovary, decidua and endometrium. Moreover vaginal secretions and amniotic fluid also has some amount of it. Normally the amount of Haptoglobin is 16–200 mg/dL and its levels decreases in case of normal pregnancy [ 14 ]. The most established cause of preterm birth is inflammation causing systemic or definite infection which ultimately leads to premature birth [ 16 , 17 ]. As an acute phase protein, Haptoglobin stimulate the expression of placental prostaglandins E2 cells. The placental PGE2 cells has significant role in stimulation of myometrial contractions, cervical ripening and fetal membrane rupture. Hence, the increased levels of Haptoglobin alpha chain causes infection induced preterm birth [ 18 ]. Proteomic analysis of first trimester maternal serums revealed abundance of Haptoglobin leading ultimately to preterm birth [ 2 ]. One of the recent studies reported that the pathogenesis of preterm premature rupture of fetal membranes (PPROM) was stimulated by increased Haptoglobin levels. Almost 30% preterm deliveries are basically due to PPROM [ 14 ]. The second potential biomarker of preterm birth is transthyretin. There is a significant link between TTR levels and gestational age during early pregnancy. By building complex with retinol binding protein 4, TTR transports retinol and influence many important physiological processes such as reproduction, immune function and normal embryonic and fetal development [ 19 ]. As compare to healthy term newborns, the premature infants have reduced level of retinol. Retinol deficiency is the significant cause of chronic lung disease such as Bronchopulmonary dysplasia [ 20 ]. Present study documented slightly significant decreased expression of transthyretin level in extremely preterm group as compared to very preterm group and control subjects of 2nd trimester pregnancy. While in case of 3rd trimester samples a highly significant decrease was observed in very preterm and extremely preterm groups when compared with healthy controls with term deliveries. Transthyretin is third most important binding protein after serum albumin and thyroxine binding globulin [ 21 ]. Transthyretin mRNA and protein expression in the human trophoblast can be identified from about 6 week’s gestation, trailed by a direct rise until 13–17 weeks prior to leveling off until term gestation. Transthyretin is a marker to diagnose fetal growth restriction and higher maternal transthyretin levels causes’ better fetal development [ 19 ]. The significant decrease in transthyretin levels in very preterm and extremely preterm groups of our study may indicate the improper growth rate in preterm infants. According to the results of our investigations, human plasma proteomics of 2nd and 3rd trimester behaved differently. The levels of TTR are pronouncedly raised in third trimester group as compared to second trimester in subjects with PTB. While, HPT presented a significant raise in 2nd trimester as compared to 3rd trimester. Hence it is suggested that in case of evaluation of PTB in 2nd trimester clinicians should evaluate the level of HPT. While TTR can be considered as diagnostic marker in subjects with 3rd trimester. Conclusion This study is highlighting the significant number of complications faced by survivors of preterm birth has to face. The marked change of Haptoglobin and transthyretin in serum samples of females with preterm delivery is significant to be used as biomarkers for highlighting the risk factor in newborns. Present study has suggested that different diagnostic testing should be used to identify untimely delivery in females of both second and third trimester due to significant difference in serum levels of transthyretin and Haptoglobin. This study will improve the approach of gynecologists in managing the patients in critical conditions. Declarations Acknowledgements We thanked the Physiology/Endocrinology lab of Institute of Zoology, University of the Punjab, for financial and technical assistance. The support of participants of this study is highly acknowledged. Funding The funding for this research was provided by Physiology/Endocrinology lab of Institute of Zoology, University of the Punjab. Competing Interests The authors declare no competing interests. Author contributions Conceptualizations, NR; Investigations, JM, SA, MAI, KM, HA, NR, TM; Writing original draft, JM, SA, MAI, NR; Writing-review and editing, JM, MAI, KM, HA, TM; Visualization, JM, SA, MAI. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Supplementary information files are submitted with manuscript. References Liong, S. et al. New biomarkers for the prediction of spontaneous preterm labour in symptomatic pregnant women: a comparison with fetal fibronectin. B.J.O.G. 122(3), 370–379. https://doi.org/10.1111/1471-0528.12993 (2015). D'Silva, A. M., Hyett, J. A. & Coorssen, J. R. Proteomic analysis offirst trimester maternal serum to identify candidate biomarkers potentially predictive of spontaneous preterm birth. J. proteomics 178, 31–42. https://doi.org/10.1016/j.jprot.2018.02.002 (2015). Georgiou, H. M., Di Quinzio, M. K., Permezel, M. & Brennecke, S. P. Predicting preterm labour: current status and future prospects. Dis. markers https://doi.org/10.1155/2015/435014 (2015). Carr, H., Cnattingius, S., Granath, F., Ludvigsson, J. F. & Edstedt Bonamy, A. K. Preterm birth and risk of heart failure up to early adulthood. J. Am. Coll. Cardiol. 69(21), 2634–2642. https://doi/full/10.1016/j.jacc.2017.03.572 (2017). Souka, A. P., Papastefanou, I., Papadopoulos, G., Chrelias, C. & Kassanos, D. Cervical length in late second and third trimesters: a mixture model for predicting delivery. Ultrasound Obstet. Gynecol. 45(3), 308–312. https://doi.org/10.1002/uog.13407 (2015). Berghella, V. et al. Cerclage for sonographic short cervix in singleton gestations without prior spontaneous preterm birth: systematic review and meta-analysis of randomized controlled trials using individual patient‐level data. Ultrasound Obstet. Gynecol. 50(5), 569–577. https://doi.org/10.1002/uog.17457 (2017). Lucaroni, F. et al. Biomarkers for predicting spontaneous preterm birth: an umbrella systematic review. J. Matern.-Fetal Neonatal Med. 31(6), 726–734. https://doi.org/10.1080/14767058.2017.1297404 (2018). Esplin, M. S. Overview of spontaneous preterm birth: a complex and multifactorial phenotype. Clin. Obstet. Gynecol. 57(3), 518–530. https://doi:10.1097/GRF.0000000000000037 (2014). Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254. https://doi.org/10.1016/0003-2697(76)90527-3 (1976). Anderson, L. & Anderson, N.G. High resolution two-dimensional electrophoresis of human plasma proteins. Proc. Natl. Acad. Sci. 74(12), 5421–5425. https://doi.org/10.1073/pnas.74.12.5421 (1977). Neuhoff, V., Arold, N., Taube, D. & Ehrhardt, W. Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using coomassie brilliant blue G-250 and R-250. Electrophoresis 9(6), 255–262. https://doi.org/10.1002/elps.1150090603 (1988). Bringans, S., Kendrick, T. S., Lui, J. & Lipscombe, R. A comparative study of the accuracy of several de novo sequencing software packages for datasets derived by matrix-assisted laser desorption/ionization and electrospray. R.C.M. 22(21), 3450–3454. https://doi.org/10.1002/rcm.3752 (2008). Conde-Agudelo, A., Papageorghiou, A. T., Kennedy, S. H. & Villar, J. Novel biomarkers for the prediction of the spontaneous preterm birth phenotype: a systematic review and meta‐analysis. B.J.O.G. 118(9), 1042–1054. https://doi.org/10.1111/j.1471-0528.2011.02923.x (2011). Buyuk, G. N., Oskovi-Kaplan, Z. A. & Durukan, H. Maternal Serum Haptoglobin Levels as a Marker of Preterm Premature Rupture of Membranes. Z Geburtshilfe Neonatolm. 225(01), 47–50. https://doi:10.1055/a-1167-4512 (2021). Khazaei, H. A. et al. Association of haptoglobin phenotypes with clinical features of preterm labor disease. Acta Med. Iran. 554–559. (2013). Romero, R. et al. The role of inflammation and infection in preterm birth. In Semin. Reprod. Med. 25(1), 021–039. https://DOI:10.1055/s-2006-956773 (2007). Challis, J. R. et al. Inflammation and pregnancy. Reprod. Sci. 16(2), 206–215. https://doi.org/10.1177/1933719108329095 (2009). McCarthy, M. E. et al. Identification of haptoglobin switch-on status in archived placental specimens indicates antenatal exposure to inflammation and potential participation of the fetus in triggering preterm birth. Placenta 62 , 50–57. https://doi.org/10.1016/j.placenta.2017.12.017 (2018). Zhu, Y., Ma, H. & Ma, W. Transthyretin and retinol-binding protein 4 in patients with fetal growth restriction. Clin. Exp. Obstet. Gynecol. 46(2), 270–273. https://doi.org/10.12891/ceog4636.2019 (2019). Chen, H. J., Hsu, C. H. & Chiang, B. L. Serum retinol levels and neonatal outcomes in preterm infants. J. Formos.Med.Assoc. 116(8), 626–633. http://dx.doi.org/10.1016/j.jfma.2017.04.019 (2017). Ingenbleek, Y. & Bernstein, L. H. Plasma transthyretin as a biomarker of lean body mass and catabolic states. ADV. NUTR. 6(5), 572–580. https://doi.org/10.3945/an.115.008508 (2015). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYINFOFILE.pdf 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-4647134","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329628342,"identity":"9b86db3e-0c37-41da-b30e-f0edb459f8c1","order_by":0,"name":"Javeria Malik","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Javeria","middleName":"","lastName":"Malik","suffix":""},{"id":329628343,"identity":"9430e5e5-4cbd-48f9-9752-ee69fa9b2476","order_by":1,"name":"Shaaf Ahmad","email":"","orcid":"","institution":"King Edward Medical University, Mayo Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaaf","middleName":"","lastName":"Ahmad","suffix":""},{"id":329628344,"identity":"201b23fd-9b6d-4d14-8fbe-7ece8b3fe3e5","order_by":2,"name":"Muhammad Amir Iqbal","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Amir","lastName":"Iqbal","suffix":""},{"id":329628345,"identity":"588f5be7-0e38-4b62-bc4e-0ed91d33f80b","order_by":3,"name":"Tamseela Mumtaz","email":"","orcid":"","institution":"Government College for Women University","correspondingAuthor":false,"prefix":"","firstName":"Tamseela","middleName":"","lastName":"Mumtaz","suffix":""},{"id":329628346,"identity":"14d6368b-2aff-4f4b-af8c-d0e460cdf621","order_by":4,"name":"Kaleem Maqsood","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Kaleem","middleName":"","lastName":"Maqsood","suffix":""},{"id":329628347,"identity":"25425b42-ccb2-4d52-8b4b-aa383cc32d80","order_by":5,"name":"Husna Ahmad","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Husna","middleName":"","lastName":"Ahmad","suffix":""},{"id":329628348,"identity":"85bda3ed-de2e-4887-90ca-15066f8d4bd0","order_by":6,"name":"Nabila Roohi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYFACHoYDD8AM5kYIfYCBgbGBkJYEMIOx2YBoLQxQLW0SRGkxZz978EBCDYM9P3tjW3VhG4Mc340Etocz8Gix7MlLOJBwjIFZsudg2+2ZbQzGkjcS2A034NFicCDH4EACGwObwY3Ettu8bQyJG4C2SD7Ap+X8G6CWfww8IC3FQC31hLXcANqS2MYgAdLCDNSSYADSgtdhN94lHEjskzAA+qVZesY5CcOZZx62G+LzvsH53MMfPnyzAYZY88HPBWU28nzHk4897MGjBQogMcIMYTC2EdYAA8xQmo14LaNgFIyCUTASAADWpVH0PDGlcgAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Punjab","correspondingAuthor":true,"prefix":"","firstName":"Nabila","middleName":"","lastName":"Roohi","suffix":""}],"badges":[],"createdAt":"2024-06-27 08:56:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4647134/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4647134/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60943308,"identity":"6a240b89-f98e-4a44-89a3-5eb39982b64c","added_by":"auto","created_at":"2024-07-23 21:59:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179678,"visible":true,"origin":"","legend":"\u003cp\u003eFlowsheet showing experimental pipeline along with bioinformatics analysis. (Protein sampling is describing spots excision from 2D gel).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/bf52a206eb78f649b1e60846.png"},{"id":60943306,"identity":"78d4a2fa-b9c6-4bce-b692-572cf514e941","added_by":"auto","created_at":"2024-07-23 21:59:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1544589,"visible":true,"origin":"","legend":"\u003cp\u003e2-DE gel of healthy pregnant female with term delivery. Spot identification was based on comparison with protein map published in world-2D page constellation.\u003c/p\u003e\n\u003cp\u003eBy isoelectric focusing (IEF) the proteins (250μg) were resolved on 17cm, 3-10 non-linear (NL) immobilized pH gradient (IPG) strips and stained by coommasie brilliant blue G-250 staining solution. For best results 10% gradient gel was selected.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eComplement Factor B, \u003csup\u003e2\u003c/sup\u003eSerotransferrin, \u003csup\u003e3\u003c/sup\u003eAlbumin, \u003csup\u003e4\u003c/sup\u003eVitamin D Binding Protein, \u003csup\u003e5\u003c/sup\u003eAlpha-1-Antitrypsin, \u003csup\u003e6\u003c/sup\u003eAlpha-2-HS-glycoprotein, \u003csup\u003e7\u003c/sup\u003eHaptoglobin Beta Chain, \u003csup\u003e8\u003c/sup\u003eComplement C3,\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e9 \u003c/sup\u003eComplement C4-B, \u003csup\u003e10\u003c/sup\u003eApolipoprotein A1, \u003csup\u003e11\u003c/sup\u003eRetinol binding protein 4, \u003csup\u003e12\u003c/sup\u003eHaptoglobin alpha chain, \u003csup\u003e13\u003c/sup\u003eTransthyretin.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/6d2d1578eaf03a7d820b11ca.png"},{"id":60944104,"identity":"98d9a171-9aa9-4905-a371-a75606f2b315","added_by":"auto","created_at":"2024-07-23 22:07:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1972315,"visible":true,"origin":"","legend":"\u003cp\u003eComparative 2D-PAGE representation of the variations in expression of \u003csup\u003e1\u003c/sup\u003eHaptoglobin alpha chain and \u003csup\u003e2\u003c/sup\u003etransthyretin in gel images of maternal serum proteome \u003cstrong\u003e(a) \u003c/strong\u003e2D gel of 2\u003csup\u003end\u003c/sup\u003e trimester pregnant female with term delivery, depicting \u003csup\u003e1\u003c/sup\u003eHaptoglobin alpha chain and \u003csup\u003e2\u003c/sup\u003etransthyretin \u003cstrong\u003e(b)\u003c/strong\u003e 2\u003csup\u003end \u003c/sup\u003etrimester pregnant female with very preterm delivery, depicting sight increase in \u003csup\u003e1\u003c/sup\u003eHaptoglobin alpha chain expression and significant rise in the expression of \u003csup\u003e2\u003c/sup\u003etransthyretin \u003cstrong\u003e(c)\u003c/strong\u003e 2\u003csup\u003end\u003c/sup\u003e trimester pregnant female with extremely preterm delivery, depicting slight decrease in \u003csup\u003e1\u003c/sup\u003eHaptoglobin alpha chain while significant reduction in \u003csup\u003e2\u003c/sup\u003etransthyretin expression \u003cstrong\u003e(d)\u003c/strong\u003e 3\u003csup\u003erd\u003c/sup\u003e trimester pregnant female with term delivery depicting increased expression of transthyretin and reduced level of Haptoglobin alpha chain as compare to 2\u003csup\u003end\u003c/sup\u003e trimester controls \u003cstrong\u003e(e) \u003c/strong\u003e3\u003csup\u003erd\u003c/sup\u003e trimester of pregnant female with very preterm delivery, depicting increase in \u003csup\u003e1\u003c/sup\u003eHaptoglobin alpha chain while reduction in the expression of \u003csup\u003e2\u003c/sup\u003etransthyretin \u003cstrong\u003e(f)\u003c/strong\u003e 3\u003csup\u003erd\u003c/sup\u003e trimester of pregnant female with extremely preterm delivery, depicting significant rise in \u003csup\u003e1\u003c/sup\u003eHaptoglobin alpha chain while almost disappearance of \u003csup\u003e2\u003c/sup\u003etransthyretin.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/9b546e3a7607cb1b17fd9d02.png"},{"id":60943303,"identity":"e4cc5458-4b79-404b-9df0-e0bfd21505f9","added_by":"auto","created_at":"2024-07-23 21:59:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188116,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical description of raw volumes (%) of identified spots in Controls, VPTB (Very\u003c/p\u003e\n\u003cp\u003ePreterm Birth) and EPTB (Extremely Preterm Birth), obtained by gel analysis through SameSpots software. \u003cstrong\u003e(Graph: 1)\u003c/strong\u003e Raw volumes of transthyretin in comparable groups of 2\u003csup\u003end \u003c/sup\u003etrimester samples with significance at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01. \u003cstrong\u003e(Graph: 2)\u003c/strong\u003e Raw volumes of Haptoglobin alpha chain in comparable groups of 2\u003csup\u003end\u003c/sup\u003e trimester samples with significance at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. \u003cstrong\u003e(Graph: 3) \u003c/strong\u003eRaw volumes of Transthyretin in comparable groups of 3\u003csup\u003erd\u003c/sup\u003e trimester samples with significance at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. \u003cstrong\u003e(Graph: 4) \u003c/strong\u003eRaw volumes of Haptoglobin alpha chain in comparable groups of 3\u003csup\u003erd \u003c/sup\u003etrimester samples with significance at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/577be6a8df992a0e9e7b970e.png"},{"id":60944105,"identity":"f720693e-c942-4248-ae19-be2fdebdc131","added_by":"auto","created_at":"2024-07-23 22:07:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1460001,"visible":true,"origin":"","legend":"\u003cp\u003eThe 3D view of raw volume of each spot obtained through gel analysis by SameSpots software. (I) Raw volume of Haptoglobin alpha chain in \u003csup\u003ea\u003c/sup\u003econtrols, \u003csup\u003eb\u003c/sup\u003every preterm and \u003csup\u003ec\u003c/sup\u003eextremely preterm group of 2\u003csup\u003end \u003c/sup\u003etrimester samples (II)\u003cstrong\u003e \u003c/strong\u003eraw volume of transthyretin in \u003csup\u003ed\u003c/sup\u003econtrols, \u003csup\u003ee\u003c/sup\u003every preterm and \u003csup\u003ef\u003c/sup\u003eextremely preterm group of 2\u003csup\u003end\u003c/sup\u003e trimester samples. (III) Raw volume of Haptoglobin alpha chain in \u003csup\u003eg\u003c/sup\u003econtrols, \u003csup\u003eh\u003c/sup\u003every preterm and \u003csup\u003ei\u003c/sup\u003eextremely preterm group of 3\u003csup\u003erd\u003c/sup\u003e trimester samples\u003cstrong\u003e \u003c/strong\u003e(IV) Raw volume of transthyretin in \u003csup\u003ej\u003c/sup\u003econtrols, \u003csup\u003ek\u003c/sup\u003every preterm and \u003csup\u003el\u003c/sup\u003eextremely preterm group of 3\u003csup\u003erd\u003c/sup\u003e trimester samples.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/cfa0061ef277c10cef6b0923.png"},{"id":68211426,"identity":"e8cd7af6-34ae-48e4-af5f-5bdabf62e923","added_by":"auto","created_at":"2024-11-04 17:38:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5227069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/622146e9-87e1-4ddc-93e2-1beb659a8682.pdf"},{"id":60943305,"identity":"49cc8dfa-3f88-4acf-bc20-3304525d41ed","added_by":"auto","created_at":"2024-07-23 21:59:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2194656,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYINFOFILE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4647134/v1/d741f71e5a8595344dcc2eb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eProteomic Profiling of Second and Third Trimester Pregnant Females Having Preterm Birth\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePreterm birth is one of the most important obstetric complication, linked with serious health outcomes like newborn mortality and morbidity, huge financial loss to society and significant emotional stress to mothers and families [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nearly, 15\u0026nbsp;million babies born preterm yearly and 28% of deaths in infancy are linked with prematurity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe survival rates have significantly increased in recent past, because of improvements in neonatology and perinatology mainly for extremely premature infants (gestational age between 24\u0026ndash;27 weeks) but unluckily the morbidity linked with survivors is still significant. About, one fifth to one quarter extremely premature newborns suffer some major disability such as blindness or deafness, impaired mental development, chronic lung diseases and cerebral palsy. late preterm newborns (gestational age between 32\u0026ndash;36 weeks) also face the risk factors such as feeding difficulties, jaundice, respiratory distress syndrome and temperature instabilities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Further, findings of earlier studies indicate a significant risk of stroke, cardiovascular mortality and hypertension in survivors of preterm birth [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe increasing number of complications compelled the obstetricians to diagnose effective therapeutic interventions that allow early disease prediction and prevention. In this regard the identification of constantly varying abundance of particular proteins may help to provide new diagnostic tools for early risk assessment of PTB [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently the diagnosis and prevention of preterm birth is established by identification of risk factor in second and third trimester of pregnancy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePTB is a multifactorial condition, with multiple hereditary, infective, inflammatory and environmental factors possibly happening in one patient [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pathophysiological paths linked with PTB are multifaceted and improperly understood. Hence, forecasting PTB has been a challenge since ages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this situation, the ideal screening model for PTB prediction is the assessment of serological biomarkers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study primarily focused on role of Haptoglobin and transthyretin for prediction of preterm birth. Subjects only with spontaneous preterm delivery will be included in this investigation. The back bone of this investigation is to uncover some of the left over secret in the manifestation of PTB by evaluation of using ultra-sensitive and well-established top down proteomic method. Moreover, establishing new biomarkers in the biology of PTB will improve our approach in managing the patients in critical conditions.\u003c/p\u003e"},{"header":"MATERIAL AND METHOD","content":"\u003ch2\u003eSample Collection\u003c/h2\u003e\n\u003cp\u003eThis study was approved by ethical review committee of Institute of Zoology, University of the Punjab. Pregnant females (n=260) in their second and third trimester were registered from public hospitals of Lahore (Sheikh Zaid Hospital, Jinnah Hospital and Lady Wellington Hospital). The purpose of the study was explained and informed consent clearly stating the study purpose was signed by the patients or their attendants. As the research involved human participants, it was performed in accordance with the relevant guidelines and regulations mentioned in the \u0026lsquo;Declaration of Helsinki\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe risk factors for preterm birth were examined by the medical history of the patients. Before sample collection each patient was asked about smoking status, age, previous medical history, parity, gravidity and complete history of miscarriages and abortion (table: 1). Systolic and diastolic blood pressure was recorded before sample collection. \u0026nbsp;The subjects of age less than 18 and more than 40 were excluded from the study. Subjects with complications during pregnancy like extensive bleeding, multiple miscarriages, fetal congenital irregularities, ectopic pregnancy and any other confounding pathology were also excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt first, 350 pregnant females were registered in the study, fifty five females did not show any interest and withdrawn from the study. Thirty five females were not sure about their last menstrual period (LMP) date. \u0026nbsp;Overall 260 females were followed till the end of their pregnancy. At their first visit, blood sample was taken and after delivery their total gestational age was noted. Out of 260, 150 females delivered preterm. First we separated them into second and third trimester samples based on time of sample collection. Further, we divided them into very preterm group (gestational age \u0026le; 32weeks) and extremely preterm group (gestational age \u0026le; 28 weeks). 100 females delivered at term with gestational age \u0026ge; 37 weeks. Out of 150, 70 females were in the second trimester at the time of sampling and were considered as control I and 80 were in the third trimester at the time of sampling and considered as control II. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"744\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.440860215053764%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic features\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.983870967741936%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003end\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;trimester samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.021505376344086%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.870967741935484%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.059139784946236%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003erd\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;trimester samples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96236559139785%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.661290322580645%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.752721617418352%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026ge;37 weeks)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003every preterm\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026le;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e32weeks)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.752721617418352%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtremely preterm \u0026nbsp; \u0026nbsp; (\u003c/strong\u003e\u003cstrong\u003e\u0026le; 28 weeks\u003c/strong\u003e\u003cstrong\u003e) \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.26438569206843%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eVALUE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(\u0026ge;37 weeks)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery preterm (\u003c/strong\u003e\u003cstrong\u003e\u0026le; 28 weeks\u003c/strong\u003e\u003cstrong\u003e) \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.841368584758943%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eextremely\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003epreterm \u0026nbsp; \u0026nbsp; \u0026nbsp; (\u003c/strong\u003e\u003cstrong\u003e\u0026le; 28 weeks\u003c/strong\u003e\u003cstrong\u003e) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.864696734059098%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.458950201884253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e26.5\u0026plusmn;5.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e26.67\u0026plusmn;6.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e28.5\u0026plusmn;2.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.882907133243608%\" valign=\"top\"\u003e\n \u003cp\u003e0.7568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e28.71\u0026plusmn;6.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e24.14\u0026plusmn;5.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e28.43\u0026plusmn;3.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6716016150740245%\" valign=\"top\"\u003e\n \u003cp\u003e0.2278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.458950201884253%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e0 =50 %\u003c/p\u003e\n \u003cp\u003e\u0026gt;0 =50 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e0 =0 %\u003c/p\u003e\n \u003cp\u003e\u0026gt;0 =100 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e0 = 25%\u003c/p\u003e\n \u003cp\u003e\u0026gt;0 = 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.882907133243608%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e0 =36 %\u003c/p\u003e\n \u003cp\u003e\u0026gt;0 =54 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e0 =26 %\u003c/p\u003e\n \u003cp\u003e\u0026gt;0 =74 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e0 =0 %\u003c/p\u003e\n \u003cp\u003e\u0026gt;0 =100 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6716016150740245%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.458950201884253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e1.250\u0026plusmn;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e2.429\u0026plusmn;1.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e2.500\u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.882907133243608%\" valign=\"top\"\u003e\n \u003cp\u003e0.2692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e2.571\u0026plusmn;2.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e2.429\u0026plusmn;1.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e3.143\u0026plusmn;2.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6716016150740245%\" valign=\"top\"\u003e\n \u003cp\u003e0.7969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.458950201884253%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.882907133243608%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6716016150740245%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.458950201884253%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaby sex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e50% Male\u003c/p\u003e\n \u003cp\u003e50%female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e28%Male\u003c/p\u003e\n \u003cp\u003e72%female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.036339165545087%\" valign=\"top\"\u003e\n \u003cp\u003e25%Male\u003c/p\u003e\n \u003cp\u003e75%female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.882907133243608%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e38%Male\u003c/p\u003e\n \u003cp\u003e62%female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e42%Male\u003c/p\u003e\n \u003cp\u003e58%female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.978465679676985%\" valign=\"top\"\u003e\n \u003cp\u003e50%Male\u003c/p\u003e\n \u003cp\u003e50%female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6716016150740245%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1: The demographic features of the study group.\u003c/p\u003e\n\u003ch2\u003eSerum Separation\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWithin 3 hours of phlebotomy the serum was separated. For serum separation, centrifugation of each blood sample was done for 10 minutes at 2500 rpm. Autoclaved cryovials were used for serum storage and labeled clearly with date and sample ID. For further processing, samples were stored at -80 ˚C. \u0026nbsp; \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein Estimation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor protein estimation, Bradford assay [9] was used. For quantification of protein, Bradford method is the highly specific and most accurate one. It is based on binding of coomassie blue\u003c/p\u003e\n\u003cp\u003eG250 with protein molecules and gives maximum absorption at 595nm.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eElectrophoretic Analysis of Proteins\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAfter protein quantification, 2-dimensional gel electrophoresis was done that involves first dimensional analysis by Iso-Electric Focusing (IEF) and second dimensional analysis by SDS PAGE [10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor iso-electric focusing, IPG strips of 17cm, linear, 3-10pH with catalog No. 163-2007 of BioRad Company were used. By using rehydration buffer samples of 250 \u0026micro;g protein/ 300 \u0026micro;L were prepared. To avoid evaporation, mineral oil was overlaid after loading the sample during rehydration. For 12hrs at 50V, rehydration was done and then focusing was performed at 20\u0026deg;C with 10000V. After the completion of iso-electric focusing, the IPG strip was equilibrated in SDS equilibration buffers 1 and 2, respectively in shaker.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor second dimensional analysis, PROTEAN II XL cell (Bio-Rad) was used. On the inner plates of electrophoresis cell, 12% polyacrylamide gel was mounted. After shifting strip to the gel, it was over laid with 0.5% low melting point agarose. After that on the +ve end of the IPG strip, protein marker (15\u0026micro;L) was loaded. \u0026nbsp; The electrophoretic cell was filled with running buffer. The gel was run firstly at 16mA and then at 35mA. After completion, the gel was transferred in fixative solution for overnight. Later on, staining of the gel was done in coomassie brilliant blue G250 as described by [11]. Finally, gel was destained in destaining solution.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eProtein Identification\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe proteins were identified by using peptide sequence databases through Mascot software search engine. Each sample was digested by trypsin to obtain peptides by set protocol [12]. After that Phase Nano-LC-MS/MS analysis was done and the peptides were separated by Shimadzu LC-20AD Nano nanoliter liquid chromatograph. Mass Spectrometry Detection was performed by ESI tandem mass spectrometer: TripleTOF 5600 (SCIEX, Framingham, MA, USA). \u0026nbsp;Finally, the spectra obtained were analyzed to identify protein of interest using Mascot 2.3.02 (Fig. 1). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;2D gel analysis by SameSpots \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAfter identification by LC-MS/MS and Mascot, Samespots software was used to determine the volume and area covered by each protein. Statistical analysis of data was done by using ANOVA Tukey\u0026rsquo;s Post Hoc analysis.\u003c/p\u003e"},{"header":"RESULTS TRYPTIC DIGESTION AND LC-MS/MS RESULTS","content":"\u003cp\u003eThe presence of potential predictive biomarkers, Haptoglobin alpha chain and transthyretin in\u003c/p\u003e\n\u003cp\u003e2DE gels of both 2\u003csup\u003end\u003c/sup\u003e and 3\u003csup\u003erd\u003c/sup\u003e trimester samples was confirmed by tryptic digestion and MALDITOF/LC-MS (Table. 2). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"749\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.533333333333335%\"\u003e\n \u003cp\u003eProtein ID\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.466666666666667%\"\u003e\n \u003cp\u003eProtein identified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2%\" valign=\"top\"\u003e\n \u003cp\u003eCalculated\u003c/p\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003cp\u003e(kDa)/pI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.8%\"\u003e\n \u003cp\u003eProtein Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\"\u003e\n \u003cp\u003eMatched peptide sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\" valign=\"top\"\u003e\n \u003cp\u003eSequence coverage Percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.533333333333333%\"\u003e\n \u003cp\u003eUnique\u003c/p\u003e\n \u003cp\u003espectra\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003eNo. \u0026nbsp; \u0026nbsp; of\u003c/p\u003e\n \u003cp\u003eunique peptides\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.533333333333335%\" valign=\"top\"\u003e\n \u003cp\u003esp|P00738|HPT_\u003c/p\u003e\n \u003cp\u003eHUMAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.466666666666667%\" valign=\"top\"\u003e\n \u003cp\u003eHaptoglobin alpha chain\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2%\"\u003e\n \u003cp\u003e45.8/6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.8%\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTEGDGVYTLNNEK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.533333333333333%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.533333333333335%\" valign=\"top\"\u003e\n \u003cp\u003esp|P02766|TTHY_\u003c/p\u003e\n \u003cp\u003eHUMAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.466666666666667%\"\u003e\n \u003cp\u003eTransthyretin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.2%\"\u003e\n \u003cp\u003e15.9/5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.8%\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVEIDTK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.533333333333333%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.666666666666666%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2: The spots identification with LC-MS/MS.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Identification of Haptoglobin Alpha Chain\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSpot no 12 was identified as Human Haptoglobin alpha chain. Mascot search result provided protein view as sp|P00738|HPT_HUMAN OX=9606 GN=HP PE=1 SV=1 with calculated MW (kDa/ pI) of protein was 45.8/6.56.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe protein score was 158 with 7 unique peptides and 52 unique spectra. The search parameters included trypsin (as enzyme) which cut C-terminal side of KR unless next residue is P. Oxidation (M) was used as variable modification.\u003c/p\u003e\n\u003cp\u003eThe total percentage of coverage of resultant protein sequence was 16.5%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"1031\" height=\"330\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe total peptide sequence obtained. Bold letters represented the matched peptide sequence of Haptoglobin alpha chain.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Identification of Transthyretin\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSpot no 13 was identified as Human transthyretin. Mascot search result provided protein view as protein id: sp|P02766|TTHY_HUMAN OX=9606 GN=TTR PE=1 SV=1 with calculated\u003c/p\u003e\n\u003cp\u003eMW (kDa/ pI) of protein was 15.9/5.58. The protein score was 160 with 7 unique peptides and 16 unique spectra. The search parameters included trypsin (as enzyme) which cut C-terminal side of KR unless next residue is P. Oxidation (M) was used as variable modification. The total percentage of coverage of resultant protein sequence was 41%\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"1039\" height=\"304\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe total peptide sequence obtained. Bold letters represented the matched peptide sequence of transthyretin.\u003c/p\u003e\n\u003ch1\u003e\u0026nbsp;\u003c/h1\u003e\n\u003ch2\u003e2DE GEL ANALYSIS\u003c/h2\u003e\n\u003cp\u003eFor 2DE gel analysis SameSpots software and Swiss 2D page database were used (fig. 2). Table 2 elaborated the details of distinct protein identified and labeled in both very preterm and extremely preterm groups in fig. 3. Raw volume of both transthyretin and Haptoglobin alpha chain was calculated by ANOVA (Tukey\u0026rsquo;s Post Hoc analysis) (fig.4). The 3D expressions of transthyretin and Haptoglobin alpha chain were identified and both very preterm and extremely preterm groups were compared with controls with term delivery (fig. 5).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePathophysiological paths linked with PTB are multifaceted and improperly understood. Hence, forecasting PTB has been a challenge since ages [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The pathogenesis of preterm birth is complex and multifactorial. Not many of the proposed biomarkers meet the standard to be considered clinically valuable for foreseeing preterm birth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn present study, Haptoglobin alpha chain and transthyretin were selected for prediction of preterm birth in pregnant females in their second and third trimester of pregnancy. The results of our study showed slight increase in Haptoglobin alpha chain levels in very preterm and extremely preterm groups as compare to controls with term delivery of 2nd trimester samples. While in case of 3rd trimester samples the Haptoglobin levels of both experimental groups showed significant increase as compare to control group. Our study also demonstrated the significantly increased level of Haptoglobin alpha chain in healthy controls of 2nd trimester pregnancy as compared to control subjects of 3rd trimester pregnancy.\u003c/p\u003e \u003cp\u003eHaptoglobin is an acute phase protein which shows marked elevation in case of acute or prolonged systemic inflammation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Haptoglobin is functionally very important as it plays important role in protection of body against bacteriostatic activity, toxic free radicals and balancing between helper T cell type 1 and helper T cell type 2 immune responses. Past studies have reported that Haptoglobin has significant role in numerous diseases like tuberculosis, preeclampsia, diabetes and Preterm birth [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBasically Haptoglobin is synthesized by liver while in miner amounts it is synthesized by ovary, decidua and endometrium. Moreover vaginal secretions and amniotic fluid also has some amount of it. Normally the amount of Haptoglobin is 16\u0026ndash;200 mg/dL and its levels decreases in case of normal pregnancy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most established cause of preterm birth is inflammation causing systemic or definite infection which ultimately leads to premature birth [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As an acute phase protein, Haptoglobin stimulate the expression of placental prostaglandins E2 cells. The placental PGE2 cells has significant role in stimulation of myometrial contractions, cervical ripening and fetal membrane rupture. Hence, the increased levels of Haptoglobin alpha chain causes infection induced preterm birth [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Proteomic analysis of first trimester maternal serums revealed abundance of Haptoglobin leading ultimately to preterm birth [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the recent studies reported that the pathogenesis of preterm premature rupture of fetal membranes (PPROM) was stimulated by increased Haptoglobin levels. Almost 30% preterm deliveries are basically due to PPROM [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe second potential biomarker of preterm birth is transthyretin. There is a significant link between TTR levels and gestational age during early pregnancy. By building complex with retinol binding protein 4, TTR transports retinol and influence many important physiological processes such as reproduction, immune function and normal embryonic and fetal development\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. As compare to healthy term newborns, the premature infants have reduced level of retinol. Retinol deficiency is the significant cause of chronic lung disease such as Bronchopulmonary dysplasia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePresent study documented slightly significant decreased expression of transthyretin level in extremely preterm group as compared to very preterm group and control subjects of 2nd trimester pregnancy. While in case of 3rd trimester samples a highly significant decrease was observed in very preterm and extremely preterm groups when compared with healthy controls with term deliveries.\u003c/p\u003e \u003cp\u003eTransthyretin is third most important binding protein after serum albumin and thyroxine binding globulin [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Transthyretin mRNA and protein expression in the human trophoblast can be identified from about 6 week\u0026rsquo;s gestation, trailed by a direct rise until 13\u0026ndash;17 weeks prior to leveling off until term gestation. Transthyretin is a marker to diagnose fetal growth restriction and higher maternal transthyretin levels causes\u0026rsquo; better fetal development [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The significant decrease in transthyretin levels in very preterm and extremely preterm groups of our study may indicate the improper growth rate in preterm infants.\u003c/p\u003e \u003cp\u003eAccording to the results of our investigations, human plasma proteomics of 2nd and 3rd trimester behaved differently. The levels of TTR are pronouncedly raised in third trimester group as compared to second trimester in subjects with PTB.\u003c/p\u003e \u003cp\u003eWhile, HPT presented a significant raise in 2nd trimester as compared to 3rd trimester. Hence it is suggested that in case of evaluation of PTB in 2nd trimester clinicians should evaluate the level of HPT. While TTR can be considered as diagnostic marker in subjects with 3rd trimester.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is highlighting the significant number of complications faced by survivors of preterm birth has to face. The marked change of Haptoglobin and transthyretin in serum samples of females with preterm delivery is significant to be used as biomarkers for highlighting the risk factor in newborns. Present study has suggested that different diagnostic testing should be used to identify untimely delivery in females of both second and third trimester due to significant difference in serum levels of transthyretin and Haptoglobin. This study will improve the approach of gynecologists in managing the patients in critical conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thanked the Physiology/Endocrinology lab of Institute of Zoology, University of the Punjab, for financial and technical assistance. The support of participants of this study is highly acknowledged.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding for this research was provided by Physiology/Endocrinology lab of Institute of Zoology, University of the Punjab.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualizations, NR; Investigations, JM, SA, MAI, KM, HA, NR, TM; Writing original draft, JM, SA, MAI, NR; Writing-review and editing, JM, MAI, KM, HA, TM; Visualization, JM, SA, MAI. All authors have read and agreed to the published version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Supplementary information files are submitted with manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiong, S. \u003cem\u003eet al.\u003c/em\u003e New biomarkers for the prediction of spontaneous preterm labour in symptomatic pregnant women: a comparison with fetal fibronectin. B.J.O.G. 122(3), 370\u0026ndash;379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1471-0528.12993\u003c/span\u003e\u003cspan address=\"10.1111/1471-0528.12993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Silva, A. M., Hyett, J. A. \u0026amp; Coorssen, J. R. Proteomic analysis offirst trimester maternal serum to identify candidate biomarkers potentially predictive of spontaneous preterm birth. J. proteomics 178, 31\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jprot.2018.02.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jprot.2018.02.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgiou, H. M., Di Quinzio, M. K., Permezel, M. \u0026amp; Brennecke, S. P. Predicting preterm labour: current status and future prospects. Dis. markers \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2015/435014\u003c/span\u003e\u003cspan address=\"10.1155/2015/435014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarr, H., Cnattingius, S., Granath, F., Ludvigsson, J. F. \u0026amp; Edstedt Bonamy, A. K. Preterm birth and risk of heart failure up to early adulthood. J. Am. Coll. Cardiol. 69(21), 2634\u0026ndash;2642. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi/full/10.1016/j.jacc.2017.03.572\u003c/span\u003e\u003cspan address=\"https://doi/full/10.1016/j.jacc.2017.03.572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouka, A. P., Papastefanou, I., Papadopoulos, G., Chrelias, C. \u0026amp; Kassanos, D. Cervical length in late second and third trimesters: a mixture model for predicting delivery. Ultrasound Obstet. Gynecol. 45(3), 308\u0026ndash;312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/uog.13407\u003c/span\u003e\u003cspan address=\"10.1002/uog.13407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerghella, V. et al. Cerclage for sonographic short cervix in singleton gestations without prior spontaneous preterm birth: systematic review and meta-analysis of randomized controlled trials using individual patient‐level data. Ultrasound Obstet. Gynecol. 50(5), 569\u0026ndash;577. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/uog.17457\u003c/span\u003e\u003cspan address=\"10.1002/uog.17457\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucaroni, F. et al. Biomarkers for predicting spontaneous preterm birth: an umbrella systematic review. J. Matern.-Fetal Neonatal Med. 31(6), 726\u0026ndash;734. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/14767058.2017.1297404\u003c/span\u003e\u003cspan address=\"10.1080/14767058.2017.1297404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsplin, M. S. Overview of spontaneous preterm birth: a complex and multifactorial phenotype. Clin. Obstet. Gynecol. 57(3), 518\u0026ndash;530. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1097/GRF.0000000000000037\u003c/span\u003e\u003cspan address=\"https://doi:10.1097/GRF.0000000000000037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248\u0026ndash;254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0003-2697(76)90527-3\u003c/span\u003e\u003cspan address=\"10.1016/0003-2697(76)90527-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1976).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson, L. \u0026amp; Anderson, N.G. High resolution two-dimensional electrophoresis of human plasma proteins. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e 74(12), 5421\u0026ndash;5425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.74.12.5421\u003c/span\u003e\u003cspan address=\"10.1073/pnas.74.12.5421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1977).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeuhoff, V., Arold, N., Taube, D. \u0026amp; Ehrhardt, W. Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using coomassie brilliant blue G-250 and R-250. Electrophoresis 9(6), 255\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/elps.1150090603\u003c/span\u003e\u003cspan address=\"10.1002/elps.1150090603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1988).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBringans, S., Kendrick, T. S., Lui, J. \u0026amp; Lipscombe, R. A comparative study of the accuracy of several de novo sequencing software packages for datasets derived by matrix-assisted laser desorption/ionization and electrospray. \u003cem\u003eR.C.M.\u003c/em\u003e 22(21), 3450\u0026ndash;3454. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/rcm.3752\u003c/span\u003e\u003cspan address=\"10.1002/rcm.3752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConde-Agudelo, A., Papageorghiou, A. T., Kennedy, S. H. \u0026amp; Villar, J. Novel biomarkers for the prediction of the spontaneous preterm birth phenotype: a systematic review and meta‐analysis. B.J.O.G. 118(9), 1042\u0026ndash;1054. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1471-0528.2011.02923.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1471-0528.2011.02923.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuyuk, G. N., Oskovi-Kaplan, Z. A. \u0026amp; Durukan, H. Maternal Serum Haptoglobin Levels as a Marker of Preterm Premature Rupture of Membranes. Z Geburtshilfe Neonatolm. 225(01), 47\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1055/a-1167-4512\u003c/span\u003e\u003cspan address=\"https://doi:10.1055/a-1167-4512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhazaei, H. A. \u003cem\u003eet al.\u003c/em\u003e Association of haptoglobin phenotypes with clinical features of preterm labor disease. Acta Med. Iran. 554\u0026ndash;559. (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomero, R. \u003cem\u003eet al.\u003c/em\u003e The role of inflammation and infection in preterm birth. In Semin. Reprod. Med. 25(1), 021\u0026ndash;039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://DOI:10.1055/s-2006-956773\u003c/span\u003e\u003cspan address=\"https://DOI:10.1055/s-2006-956773\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChallis, J. R. et al. Inflammation and pregnancy. Reprod. Sci. 16(2), 206\u0026ndash;215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1933719108329095\u003c/span\u003e\u003cspan address=\"10.1177/1933719108329095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCarthy, M. E. \u003cem\u003eet al.\u003c/em\u003e Identification of haptoglobin switch-on status in archived placental specimens indicates antenatal exposure to inflammation and potential participation of the fetus in triggering preterm birth. Placenta \u003cem\u003e62\u003c/em\u003e, 50\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.placenta.2017.12.017\u003c/span\u003e\u003cspan address=\"10.1016/j.placenta.2017.12.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, Y., Ma, H. \u0026amp; Ma, W. Transthyretin and retinol-binding protein 4 in patients with fetal growth restriction. Clin. Exp. Obstet. Gynecol. 46(2), 270\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.12891/ceog4636.2019\u003c/span\u003e\u003cspan address=\"10.12891/ceog4636.2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, H. J., Hsu, C. H. \u0026amp; Chiang, B. L. Serum retinol levels and neonatal outcomes in preterm infants. J. Formos.Med.Assoc. 116(8), 626\u0026ndash;633. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1016/j.jfma.2017.04.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jfma.2017.04.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngenbleek, Y. \u0026amp; Bernstein, L. H. Plasma transthyretin as a biomarker of lean body mass and catabolic states. ADV. NUTR. 6(5), 572\u0026ndash;580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3945/an.115.008508\u003c/span\u003e\u003cspan address=\"10.3945/an.115.008508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\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":false,"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":"Preterm Birth, Gestational Age, Transthyretin, Haptoglobin Alpha Chain, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-4647134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4647134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePreterm birth is the most important obstetric complication, linked with serious health outcomes, huge financial loss and emotional stress to families. The study group included 200 females with second and third trimester of pregnancy. Out of total, 70 females delivered preterm. The samples containing proteins of interests were resolved by 2D-PAGE and after tryptic digestion LC-MS/MS analysis was done. The proteins were identified by Mascot software search engine. SameSpots software was employed to determine the raw volume of proteins. Statistical analysis was done by ANOVA. The presence of potential predictive biomarkers, Haptoglobin alpha chain and transthyretin in 2DE gels of both 2nd and 3rd trimester samples was confirmed. The results of our study showed slight increase in Haptoglobin alpha chain levels in very preterm and extremely preterm groups of 2nd trimester samples. While, in case of 3rd trimester samples the Haptoglobin levels showed significant increase in experimental groups. Considerable decreased expression of transthyretin level in extremely preterm group as compared to very preterm group and control subjects of 2nd trimester pregnancy was detected. While, in case of 3rd trimester samples a gradual significant decrease was observed in experimental groups.\u003c/p\u003e \u003cp\u003eThe significantly reduced level of transthyretin and increased level of Haptoglobin alpha chain as compared to healthy term pregnancy, will serve as major risk predictor of reduced gestational age.\u003c/p\u003e","manuscriptTitle":"Proteomic Profiling of Second and Third Trimester Pregnant Females Having Preterm Birth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 21:59:39","doi":"10.21203/rs.3.rs-4647134/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":"e96902d1-4325-4df0-adb0-64f7d97a67b3","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34998448,"name":"Biological sciences/Physiology"},{"id":34998449,"name":"Health sciences/Biomarkers"},{"id":34998450,"name":"Health sciences/Risk factors"},{"id":34998451,"name":"Health sciences/Signs and symptoms"}],"tags":[],"updatedAt":"2024-11-04T17:38:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 21:59:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4647134","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4647134","identity":"rs-4647134","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}