{"paper_id":"1de49710-8a4d-47cc-a23f-b456a7083d0c","body_text":"of \nVolume 2 Issue 3 September 2012 \nISSN 2158-0510 \nwww.ijbm.org \nInternational Journal \nIn this issue \n \nEpicardial Fat Thickness Defining in Obese Patients \nSurgical Treatment of Liver Laceration in Blunt Abdominal Trauma \nMolecular Phenotyping of Reproductive Aging in Women \nBasis of Functional Reserves in Native Peoples of the Arctic \nJoint Lesions in Co-Morbidity of Osteoarthritis and Osteoporosis \nPreoperative Activation Hemostasis in Large Joint Arthroplasty \nSTI and Chronic Cystitis Complicated With Bladder Leukoplakia \nPolymorphism of Cell Cycle Regulating Genes and the Risk of Lung Cancer \nAnti-Cytokine Therapy in Psoriasis Patients \nDiagnosis of Tuberculosis of Intrathoracic Lymph Nodes in Children \nExudative Otitis Media in HIV Infected Children \nAtopic and Nonatopic Asthma in Children \nBiochemical Changes Induced By Emphysema in Children \nEpidermal Melanocytes, Langerhans Cells and Epidermal Cambial Cells \nIncreasing of Activity of Pharmacons by Lichen Thallus \nPharmaceutical Aspects of the Drug Selection \n«Soft-Start» Polymerization Technique in Dental Composite Restoration \n\n\nInternational \nJournal of \nBiomedicine \nInternational Journal of Biomedicine 2(3) (2012) 174-178\nClinical Research\nMolecular Phenotyping of Reproductive Aging in Women with \nSubfertility and Infertility\nIrina V . Sarvilina, PhD, ScD1*, Mikhail Yu. Gilyanovskiy, PhD1,\nDaria Yu. Gordienko2\n1Medical Centre «Novomeditsina», Rostov-on-Don, Russian Federation, \n2St. Petersburg State Budgetary Health Care Organization «Municipal Hospital #2»,\nSaint-Petersburg, Russian Federation\n________________________________________________________________________\nAbstract\nThere is no scientific data on the molecular mechanisms of reproductive aging in women. The purpose of the study is the development \nof molecular phenotypes of the aging in women with gynecologic pathology of reproductive and post-reproductive periods. The \npresent study included 90 women with subfertility of varying degrees of severity and infertility. All the patients were divided into \nthree groups: I group (n=30) included patients with a weak degree of subfertility; II group (n=30) with middle and high degree of \nsubfertility; III group (n=30) with clinical infertility. All the patients underwent the routine diagnosis of the sub- and infertility. \nMolecular phenotyping of blood serum and cervicovaginal fluid processed with methods in proteomics: the prefractionation, \nthe separation of proteins with standard sets, MALDI-TOF-MS/MS. Bioinformatics analysis of the molecules in the biosamples \nwas based on the integrated database «Bioinformatic Harvester». Statistical analysis of the survey data was performed using the \nsoftware «Statistica 7.0». Proteomic analysis helps in the detection of differences in the component composition of the serum \nproteins and CVF in women with subfertility of varying degrees of severity and infertility compared with the control group of \nfertile women. We have data on the universal molecular pathways of the development of reproductive aging in women with \nsubfertility and infertility. IJBM 2012; 2(3):174-178. © 2012 International Medical Research and Development Corporation. All \nrights reserved.\nKey words: proteomics, bioinformatics, molecular interactions.\nIntroduction\nThe purpose of this study is the development of \nmolecular phenotypes of aging in women with a gynecologic \npathology of the reproductive and post-reproductive periods.\nThe concept of «the aging of a person» is closely \nconnected with concepts of «reproductive health» and \n«reproductive aging». The main clinical phenotypes of \nreproductive aging in the female organism are connected with \nthe development of gynecological diseases and conditions, \nincluding uterine fibroid tumors, ovarian cysts, polycystic \novary syndrome, endometriosis in women of reproductive \nage, inflammatory diseases of the female genitals and \nmenstrual disorders among young girls, miscarriage and \nother complications of pregnancy and childbirth, subfertility \nand infertility, oncological pathology in women, diseases \npertinent to women during pre- and post-menopause, \nincluding several other female reproductive system \nabnormalities [1].\nReproductive aging of the female organism in \ndemographic researches is presented by indicators such as \nbirth rate, mortality, the birth of a child in middle age, and \nthe time prior to pregnancy (TTP). In the 21 st century, two \n______________________________________________\n*Corresponding author: Irina V . Sarvilina, PhD, ScD, \nMedical Centre «Novomeditsina», 186, Gorky str., apt. # 55, \nRostov-on-Don, Russian Federation.\nTel: 7-903-4364866; 7-863-2003073.\nE-mail: isarvilina@mail.ru\n\ntendencies became obvious in Russia, with respect to the \nbirth rate: the reduction of the number of children per family \nand the displacement of the period of childbirth in women \nto a later age. The success of the birth of the second child by \na woman until 40 years, decreases as a rule, correlating with \nthe progress of the processes of subfertility and infertility, as \nclinical phenotypes of reproductive aging.\nToday, some molecular interactions in the \nmechanism of human aging have been identified. The fields \nof female reproductive biology best studied by applying \nproteomics, include gynecological cancers, endometriosis \nand infertility [2, 3]. There is no scientific data about the \nmolecular mechanisms of reproductive aging in women. The \ncreation of the database of new intermolecular interactions \nand molecular targets for subsequent development of new \ndrugs for the prevention of reproductive aging in women is \nbecoming highly relevant.\nMaterial and Methods\nThe present study included 90 women with subfertility \nof varying degrees of severity and infertility, according to the \nWHO criteria (1996), data of Habbema J. et al., and the value \nof TTP . The control group consisted of 30 women based on \nthe next criteria, which included women with two or/and \nmore safe pregnancies within a year of sexual activity without \ncontraception. The women of the control group, included \nthose with three or less pregnancies with no spontaneous \nabortions, although with four and more pregnancies they \ncould have had spontaneous abortion. The average age of the \nwomen with subfertility and infertility was 36±1.2 years of \nage, whereas the women with fertility in the control group \nwere 35±1.1 years of age. All the patients were divided \ninto three groups: I group (n=30) included patients with a \nweak degree of subfertility, TTP=6 unsuccessful cycles; II \ngroup (n=30) with middle and high degree of subfertility, \nTTP=12 unsuccessful cycles; III group (n=30), TTP=48 \nmonths, with clinical infertility. All the patients underwent \nthe routine diagnosis of infertility, including, total and \nspecial gynecological examination; ultrasound examination \nof the female pelvic organs; determination of blood group \nand Rh-factor; complete blood count; blood tests for \nsyphilis, HIV , hepatitis В and С; the study of biocenosis \nof the urethra, vagina and cervix; hysterosalpingoscopy \nand laparoscopy on indications; endometrial biopsy; \nbacteriological examination and polymerase chain reaction \nof the urethral and cervicovaginal secretions; cervical smear \ntests; the definition of concentrations of follicle-stimulating \nhormone, luteinizing hormone, estradiol, prolactin, \ntestosterone, cortisol, progesterone, triiodothyronine, \nthyroxine, thyrotropin, somatotropin (ADVIA CENTAUR, \nBayer Diagnostics, Germany); the detection of antisperm \nantibodies and antiphospholipid antibodies; screening \nfor infections (Chlamydia trachomatis, Ureaplasma \nurealyticum, Mycoplasma genitalium, Herpes simplex virus, \nCytomegalovirus, Toxoplasma gondii, Rubella virus); the \ntyping of HLA-А, -B, -C, -DR-antigens of lymphocytes of \nthe pair; the study of genetic factors of habitual miscarriage \nand risks (G20210A prothrombin gene mutation, factor V \nG1691A gene mutations, MTHFR C677T gene mutation; \nICycler IQ-5, BioRad, USA). Molecular phenotyping of \nbiosamples (blood serum, CVF) processed with methods in \nproteomics: the prefractionation, the separation of proteins \nwith standard sets (MB-HIC C8 Kit, MB-IMAC Cu, MB-\nWax Kit, «Bruker», USA), matrix-assisted laser desorption-\nionization time-of-flight mass spectrometry (MALDI-\nTOF-MS/MS, Ultraflex II, «Bruker», USA). The partially \nidentified sequences were then submitted to «BLAST \nprotein-protein» and screened against the Homo sapiens \nSwissprot database to check if this identification matched the \nMASCOT-identification (Matrix Science). Bioinformatics \nanalysis of the molecules in the biosamples was based on the \nintegrated database “Bioinformatic Harvester” (Karlsruhe \nInstitute of Technology, Germany). The data of the molecular \ninteractions and functional features of proteins were received \nwith STRING 8.1 and STITCH databases. Statistical analysis \nof the survey data was performed using the software \n«Statistica 7.0».\nResults\nThe study revealed several forms of female subfertility \nand infertility, significantly, endocrine subfertility and \ninfertility, associated with the violation of ovulation (32%); \nFallopian tube subfertility and infertility (28%); gynecological \ndiseases as the causes of the subfertility and infertility (25%); \nimmune subfertility and infertility (1%); and unexplained \nsubfertility and infertility (14%).\nThe compatibility was for one antigen HLA-DQ in \n14 out of 90 pairs (15.5%) with subfertility (4 pairs; 4.4%) \nand clinical infertility (10 pairs; 11.1%), for HLA-В - in 21 \nout of 90 pairs (23.3%) with subfertility (9 pairs; 10%) and \nclinical infertility (12 pairs; 13.3%). Combinations for HLA \nin infertility were found in 35 out of 90 pairs (38.9%).\nFactor V G1691A gene mutations were heterozygous \nin two women with subfertility and four women with clinical \ninfertility, and in one woman of the control group. G20210A \nprothrombin gene mutation has not been identified. The \nanalysis of MTHFR C677T gene mutation rare allele 677T \nwas found in 21 women of I group (70%): in homozygous \n- in 4 women (13.3%), in the heterozygous – in 17 women \n(56.7%); in 30 women of II group (100%): in homozygous - \nin 5 women (16.7%), in heterozygous – in 25 women (83.3%); \nin 30 women of III group (100%): in homozygous - in 7 \nwomen (23.3%), in heterozygous – in 23 women (76.7%); in \n7 women of the control group (23.3%): in homozygous - in \n2 women (66.7%), in heterozygous – in 5 women (16.7%). \nProteomic analysis helps in the detection of differences \nin the component composition of the serum proteins and \ncervicovaginal fluid (CVF) in women with subfertility of \nvarying degrees of severity and infertility compared with the \ncontrol group of fertile women (Table 1, 2).\nBioinformatics analysis revealed the presence of \nmolecules, which are the participants of the universal \npathways of reproductive aging in women and the molecular \ninteractions involved.\nI. V . Sarvilina et al. / International Journal of Biomedicine 2(3) (2012) 174-178 175\n\nI. V . Sarvilina et al. / International Journal of Biomedicine 2(3) (2012) 174-178176\nDiscussion\nProteomic analysis has revealed an increase in \nthe absolute number of women with protein expression \nperforming certain biological functions and having various \nlocalizations in the intra - and extracellular spaces (Table 1, \n2).\nMolecules was seen to interact among themselves and \nwith other molecules as participants of universal pathways \nin reproductive aging in women, which is the chief cause \nfor subfertility and infertility: the hormonal signaling \npathway (insulin and insulin-like signaling, growth hormone \nsignaling, steroid signaling, Klotho signaling, AC5, TGFβ), \nnutrient sensing and signaling pathway (sirtuin deacetylases, \nAMP-activated protein kinase, TOR and translation \nsignaling, FOXA/PHA-4 transcription factor signaling, \nNRF1/SKN-1 signaling), mitochondria and ROS signaling \n(electron-transport chain signaling, stress-induced protein \nkinases: JNK and MST-1), genome surveillance pathways \n(tumor suppressors and antagonistic pleiotropy).\nEach protein molecule in the functional group \ninteracts with other protein molecules. For example, the \nTable 1\nQualitative profile of serum proteins in women with the subfertility, infertility and in control group of fertility women\nProtein name\nn (the number of women with the expression of the \nserum protein) Functional process\n(source: Bioinformatic \nHarvester, KIT, Germany)\nControl \ngroup\n(n=30)\nI \ngroup\n(n=30)\nII\ngroup\n(n=30)\nIII\ngroup\n(n=30)\n1M W,\n2Dа\n1 Beta-defensin 2 30 12 10 2 7820\nImmunity and defense, \nnucleoside, nucleotide and \nnucleic acid metabolism\n2 Metalloproteinase inhibitor 1 0 7 24 30 23794\nDevelopmental processes, \nprotein metabolism and \nmodification\n3 Cystatin-C 2 15 18 28 13300 Protein metabolism and \nmodification\n4 Apolipoprotein A-II 0 6 23 30 11175 Lipid, fatty acid and steroid \nmetabolism, transport\n5 Apolipoprotein B-100 1 5 14 30 515605 Lipid, fatty acid and steroid \nmetabolism, transport\n6 Glucose-6-phosphate \nisomerase 30 21 14 3 59991 Carbohydrate metabolism\n7 Glycodelin 1 15 18 30 16361 Developmental processes\n8 Macrophage migration \ninhibitory factor 0 22 26 30 12476 Immunity and defense\n9 Metalloproteinase inhibitor 2 1 25 29 30 72000 Protein metabolism and \nmodification\n10\nCarcinoembryonic antigen-\nrelated cell adhesion molecule \n8 precursor\n0 0 14 30 38154 Cell adhesion, signal \ntransduction\n11 Repetin 0 20 22 27 90731\nCell proliferation \nand differentiation, \ndevelopmental processes\n12 Kallikrein-14 2 17 22 26 29122 Protein metabolism and \nmodification\nNotes: 1MW - molecular weight; 2Da - Dalton.\n\nI. V . Sarvilina et al. / International Journal of Biomedicine 2(3) (2012) 174-178 177\nTable 2\nQualitative profile of proteins of cervicovaginal fluid in women with the subfertility, infertility and in control group\nProtein name\nn (the number of women with the expression of the \nprotein in CVF1) Functional process\n(source: Bioinformatic \nHarvester, KIT, \nGermany)\nCellular \nlocalizationControl \ngroup\n(n=30)\nI group\n(n=30)\nII group\n(n=30)\nIII\ngroup \n(n=30)\n2MW , \n3Dа\n1\nPutative tropomyosin \nalpha-3 chain-like \nprotein\n15 27 28 24 26269 Cell structure and \nmobility Cytoplasm\n2\nVoltage-dependent P/Q-\ntype calcium channel \nsubunit alpha-1A\n12 16 27 26 282365\nMuscle contraction, \nneuronal activities, \ntransport\nNucleus\n3 Actin-related protein \n2/3 complex subunit 2 30 22 6 4 40950\nProtein metabolism \nand modification, cell \nstructure and mobility\nCytoskeleton\n4 Histone H2B type 1-K 30 10 8 2 13890\nImmunity and defense, \nnucleoside, nucleotide \nand nucleic acid \nmetabolism\nNucleus\n5 Probable phospholipid-\ntransporting ATPase VB 20 25 25 27 165391\nLipid, fatty acid and \nsteroid metabolism,\ntransport\nMembrane\n6 Hemoglobin subunit \ndelta 8 12 14 18 16055\nBlood circulation and \ngas exchange,\ntransport\nCytoplasm\n7 Tropomyosin beta chain 30 27 25 23 32851\nCell structure and \nmobility,\ndevelopmental processes, \nmuscle contraction\nCytoskeleton\n8 Clathrin light chain B 5 8 21 30 35000 Intracellular protein \ntraffic\nGolgi\nApparatus\n9 Carboxypeptidase M 0 2 4 30 36520 Protein metabolism and \nmodification Membrane\n10\nB-Raf proto-oncogene \nserine/threonine-\nprotein kinase\n0 4 9 30 84437\nApoptosis, cell \nproliferation and \ndifferentiation, \noncogenesis, signal \ntransduction\nMembrane\n11 Small proline-rich \nprotein 2E 12 16 17 21 7855\nDevelopmental \nprocesses, cell \nproliferation and \ndifferentiation\nCytoskeleton\n12 Peroxiredoxin-6 3 23 28 28 27100 Immunity and defense Cytoplasm\n13\nCarnitine O- palmitoyl-\ntransferase 2, \nmitochondrial\n6 15 27 24 73777\nAmino acid metabolism, \nlipid, fatty acid and \nsteroid metabolism\nEnvelope\n14 Nesprin-2 1 18 26 26 796442 Cell structure and \nmobility Nucleus\n15 Toll-like receptor 7 \nprecursor 5 12 17 28 120922\nDevelopmental \nprocesses, signal \ntransduction\nMembrane\nNotes: 1CVF - cervicovaginal fluid, 2MW - molecular weight; 3Da - Dalton.\n\nI. V . Sarvilina et al. / International Journal of Biomedicine 2(3) (2012) 174-178178\nmolecular interactions of glycodelin or progestagen-\nassociated endometrial protein, are presented (Fig. 1). The \nconcentration of glycodelin rises in the serum of infertile \nwomen with abnormal tubes compared with fertile controls. \nConclusion\nWe identified potentially new biomarkers that differ \namong women with subfertility and infertility and that could \naid in developing a noninvasive, serum-based diagnostic test. \nThis study is the first step in the identification of potentially \nnew biomarkers of reproductive aging in women. Future \nidentification of the proteins and further validation in a \nsecond population is needed before these findings can be \napplied in clinical practice.\nReferences\n1. Seeber B, Sammel MD, Fan X, Gerton GL, Shaunik A, \nChittams J, Barnhart KT. Proteomic analysis of serum \nyields six candidate proteins that are differentially \nregulated in a subset of women with endometriosis. \nFertil Steril 2010; 93(7):2137-44.\n2. Anderson NL. The clinical plasma proteome: a survey \nof clinical assays for proteins in plasma and serum. \nClinical Chemistry 2010; 56 (2):177-185.\n3. Hong Zhang, Yidong Niu, Jie Feng, Huifang Guo, \nXue Y e, Heng Cui. Use of proteomic analysis of \nendometriosis to identify different protein expression \nin patients with endometriosis versus normal controls. \nFertil Steril 2006; 86(2):274-282.\nFigure 1\nMolecular interactions of progestagen-associated endometrial protein (glycodelin) (STRING 8.1 database).\nNotes: PAEP - progestagen-associated endometrial protein (glycodelin); TNFRSF14 - tumor necrosis factor receptor superfamily, \nmember 14; DARC - Duffy blood group, chemokine receptor; GH1 - growth hormone 1; PVRL1 - poliovirus receptor related 1; GYPC \nglycophorin C; PVRL2 - poliovirus receptor-related 2; FUT5 - fucosyltransferase 5; GDF7 - growth differentiation factor 7; GDF5 - \ngrowth differentiation factor 5; KIFAP3 - kinesin-associated protein 3.","source_license":"CC0","license_restricted":false}