Proteomics in prenatal diagnosis

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sional protein identification technology coupled with MS were applied to compare related samples, such as amniotic fluid. (AF) obtained at different times during.
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Proteomics in prenatal diagnosis Expert Rev. Proteomics 6(2), 111–113 (2009)

Aggeliki Kolialexi Department of Medical Genetics, Athens University School of Medicine, Athens, Greece [email protected]

George T Tsangaris Proteomics Research Unit, Centre of Basic Research II, Biomedical Research Foundation, Academy of Athens, Athens, Greece gthtsangaris@ bioacademy.gr

Ariadni Mavrou Author for correspondence

Professor Ariadni Mavrou, Department of Medical Genetics, Athens University School of Medicine, Athens, Greece Tel.: + 210 746 7463 Fax: + 210 779 5553 [email protected]

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“The goal is to identify biomarkers that are fetal-specific and differ between an aneuploid and a normal pregnancy.” The opportunities for applying proteomics in clinical practice are formidable. Particularly promising areas for research include the development of novel biomarkers for the diagnosis and early detection of disease, the identification of new targets for therapeutics and the potential for accelerating drug development through more effective strategies to evaluate therapeutic efficacy and toxicity profiles. A very exciting area for proteomics appears to be in the field of human reproduction. Pregnancy progression and delivery depend on a complex interaction of intracellular and extracellular signals, which include hormones, adhesion molecules, growth factors and immuno­modulators [1] . An intricate balance of these factors is required throughout pregnancy. In cases of fetal genetic abnormalities, this balance may be disturbed, and identification of relevant markers may be used to detect a specific type of pathology or to ascertain its severity [2] . With regards to prenatal diagnosis, proteo­ mic technology has only recently begun to be employed for the detection of fetuses at risk of chromosomal abnormalities. The goal is to identify biomarkers that are fetalspecific and differ between an aneuploid and a normal pregnancy. If detected early, they can be used as suitable disease markers for pregnancies that need to undergo prenatal diagnosis, whereas those detected at later stages are likely to be more specific and may be closely related to the phenotype of the disease. Since proteomic strategies investigate multiple molecules simultaneously, they can potentially lead to the discovery of a panel of markers with sufficient sensitivity and specificity for clinical applications. Proteomic analysis can be divided into two major groups: techniques used for profiling and techniques used for differential protein detection. The most common approach for the ana­lysis of

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reproduction-related biological f luids relies upon a coordinated use of 2DE, image ana­lysis, mass spectrometry (MS) protein identification and bio­informatics/ database construction [3,4,5] . In addition, fluorescence 2D DIGE and multidimensional protein identification technology coupled with MS were applied to compare related samples, such as amniotic fluid (AF) obtained at different times during pregnancy and maternal serum obtained from women carrying chromosomally normal and Down syndrome (DS) fetuses [6] . Emerging proteomic techniques, however, such as top-down/bottom-up and peptidomics, have not yet been applied in reproduction-related research. Proteomic studies of normal amniotic fluid

The majority of proteomic studies that aim to identify biomarkers for prenatal diagnosis focus on the ana­lysis of AF since this biologic material is routinely used for the prenatal detection of fetal genetic diseases. Proteomic profiles of AF have been generated by several groups. It must be noted, however, that different investigators not only utilized different proteomic platforms, as well as different protein databases, but have also used different levels of stringency for protein identification, making it difficult to assess the accuracy of each dataset. In 1997, Liberatori et al. identified human AF proteins in AF supernatant by immunoblot ana­lysis and reported a 2DE protein map of human AF in the second trimester of gestation [3] . In 2004, Nilsson et al. identified 43 proteins in AF at weeks 15–18 of gestation by direct ana­lysis of digested samples, followed by Fourier transform ion cyclotron resonance MS [7] . In 2006, three groups analyzed normal AF supernatant. Park et  al., using 2DE followed by MALDI-TOF MS, reported

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37 proteins [4] . Michel et al. identified 69 proteins from albumindepleted AF using Off-GelTM electro­phoresis/liquid chromatography (LC)-MS/MS [8] . Tsangaris et al. reported on the proteomic map of the AF supernatant, which is comprised of 136 different gene products, using 2DE followed by MALDI-MS/MS [5] . Queloz et al. [9] and Michaels et al. [10] compared the proteomic profiles of normal AF obtained at different weeks of gestation and reported on alterations in protein expression during pregnancy. In 2007, Cho et al. published the most extensive protein profile of second-trimester, normal human AF, which is comprised of 1026 unique gene products from 842 different genes [11] . This list includes most of the currently used biomarkers for the prenatal detection of fetal aneuploidies, such as β-hCG and a-fetoprotein. Regarding proteins produced by AF cells, Tsangaris et al. used 2DE and MS to report on a protein database of normal human amniocytes that consisted of 432 different gene products [12] . Oh et al. investigated metabolic enzymes in cultured amniocytes and reported on a possible screening method for metabolic enzymerelated proteins, which may form the basis of future metabolic screening of the fetus when amniocentesis is carried out [13] . Impact of proteomic technology on biomarker discovery for fetal aneuploidies

An AF fingerprint was generated by Wang et al. using a large variety of surface arrays in 20 samples obtained from pregnant women known to carry an aneuploid fetus [14] . Following the application of pattern-recognition algorithms, it was possible to successfully identify aneuploid fetuses based on distinct biomarker peaks that segregated at 2.65–7.0 kd; however, differentiating between the types of chromosomal abnormalities was not possible. Oh et al., using proteomic techniques to identify metabolic changes in AF, also detected proteomic changes in AF samples from pregnant women who carried DS fetuses [13] . Specifically, they noted a significant derangement in the carbohydrate and amino acid handling, and purine and intermediary metabolism, as well as miscellaneous metabolic pathways. In another study, Tsangaris et al. applied MS/MS-based proteomics to identify and compare biomarkers in AF samples from DS pregnancies with chromosomally normal fetuses [15] . This comparison revealed seven proteins that were differentially expressed in samples obtained from pregnancies with DS fetuses, compared with the control group. The more significant finding of this study, however, was the discovery that splicing factor arginine/ serine-rich 4 was only identified in samples with a DS fetus. The same group, in another study using the same MS-based technology, identified seven biomarkers for Turner syndrome in amniotic fluid samples obtained from pregnancies with Turner syndrome fetuses [16] . Serotransferin, lumican, plasma retinolbinding protein and apolipoprotein (Apo) AI levels were found to be increased in Turner syndrome fetuses, whereas levels of kininogen, prothrombin and Apo  AIV were decreased. The altered expression of these proteins may be due to the presence of one instead of two copies of the X chromosome, which influences the expression, translation and level of transcription factors of genes located either on the X chromosome or on autosomes. 112

Recently, Wang et al. used 2DE followed by MS to identify proteins that were differentially expressed in AF of DS and trisomy 18 fetuses. The proteins with significant differential expression in DS were Apo AI, SERPINA3, prealbumin (transthyretin) and transferrin [17] . Levels of Apo AI and antitrypsin were significantly decreased in trisomy 18 AF, whereas placental protein-14 was increased. On the other hand, Apo AI was decreased in DS AF, but antitrypsin, pre­albumin and transferrin were increased. Subsequent biologic network analyses revealed that proteins of the trisomy 18 AF network were involved in immune processes, dysfunction of skin pigmentation and platelet disorders, whereas those of trisomy 21 were associated with dysfunctional lipid and cholesterol metabolism, processes of metal ion transport, ATP metabolism and energy-coupled protein transport. It is again worth noting that genes controlling the production of these proteins are not located on chromosomes 21 or 18, respectively. Biomarkers for noninvasive prenatal diagnosis of fetal aneuploidies

Identification of proteins released from the placenta and the fetus into the maternal circulation may permit accurate and reliable non­invasive prenatal diagnosis and maternal serum screening for fetal aneuploidies. Until now, research for the identification of bio­ markers for noninvasive prenatal diagnosis has been applied using maternal peripheral blood, while other biological materials, such as urine, have not, as yet, been investigated. DS, which occurs in one out of 800 live births and is directly related to advanced maternal age, is the main focus of attention. Nagalla et al. used, for the first time, MS technology to identify biomarkers for DS noninvasive prenatal diagnosis [6] . They analyzed first- and second-trimester maternal serum samples of DS and gestational age-matched controls, with multiple complementary proteomic approaches, including 2D-DIGE, 2D LC-chromatofocusing, multidimensional protein identification technology, LC/LC-MS/MS and MALDI-TOF-MS peptide profiling. They found 28 and 26 proteins differentially present in the first- and second-trimester samples, respectively. Of those, 19 proteins were specific for the first and 16 were specific for the second trimester, with ten differentially present in both trimesters. They also reported that ana­lysis of MALDI-TOF-MS peptide profiles with pattern-recognition software could discriminate between DS and controls in both trimesters, with an average recognition capability approaching 96%. In 2008, proteomic ana­lysis of maternal serum was performed by Kolialexi et al. on samples from DS and control pregnancies in the second trimester by 2DE and MALDI-TOF-MS [18] . Gel comparison revealed a set of nine differentially expressed proteins in women carrying a DS fetus compared with those carrying normal fetuses. A total of eight of these proteins (transthyretin, ceruloplasmin, afamin, a-1-microglobulin, Apo E, serum amyloid P-component, histidine-rich glycoprotein and a-1-antitrypsin) were found to be upregulated, and one (clusterin) was downregulated. Three of these proteins are associated with the DS phenotype while the other six are predominantly involved in fetal growth and development. Expert Rev. Proteomics 6(2), (2009)

Proteomics in prenatal diagnosis

Conclusion

Researchers remain hopeful that proteomic studies will allow for the identification of either one protein marker or of a panel of markers for prenatal diagnosis that could be usefully employed for diagnostic purposes or for improving current screening methods.

“In complex samples, such as AF and peripheral blood samples, the use of analytical techniques that allow rapid screening and require a small quantity of sample for accurate protein identification are of great importance.” It is important to note that experimental design using standard protocols, followed by appropriate analytical techniques and statistical ana­lyses, are critical to proteomic experiments. In complex samples, such as AF and peripheral blood samples, the use of analytical techniques that allow rapid screening and require a small quantity of sample for accurate protein identification are of great importance. Of equal importance is the use of protocols that guarantee identical sample collection and proper storage conditions to ensure reproducibility. Emphasis should be placed on protein concentration, sample purification, protein digestion, as well as affinity capture and sample fractionation, so as to reduce the complexity References 1

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Pellicer A, Albert C, Mercader A et al. The pathogenesis of ovarian hyperstimulation syndrome: in vivo studies investigating the role of interleukin-1b, interleukin-6, and vascular endothelial growth factor. Fertil. Steril. 71, 482–489 (1999). Wunder DM, Kretschmer R, Bersinger NA. Concentrations of leptin and C-reactive protein in serum and follicular fluid during assisted reproductive cycles. Hum. Reprod. 20, 1266–1271 (2005). Liberatori S, Bini L, De Felice C et al. A two-dimensional protein map of human amniotic fluid at 17 weeks’ gestation. Electrophoresis 18, 2816–2822 (1997). Park SJ, Yoon WG, Song JS et al. Proteome ana­lysis of human amnion and amniotic fluid by two-dimensional electrophoresis and matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry. Proteomics 6, 349–363 (2006). Tsangaris GT, Kolialexi A, Karamessinis PM et al. The normal human amniotic fluid supernatant proteome. In Vivo 20, 479–490 (2006). Nagalla SR, Canick JA, Jacob T et al. Proteomic ana­lysis of maternal serum in Down syndrome: identification of novel protein biomarkers. J. Proteome Res. 6, 1245–1257 (2007).

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of the target fluid. It must also be noted that when identifying biomarkers for prenatal diagnosis, gestational age-matched controls must be used, since it has been proven that the protein content of samples depends on the developmental stage of the fetus. The major challenge in the field of proteomics lies between the discovery of the protein and validation of said target. For the greatest predictive power, potential biomarkers should be selected for further comparative ana­lysis of expression and structural modifications in large numbers of samples from chromosomally normal and abnormal pregnancies obtained from different populations. It is also important to compare the sensitivity and specificity of the new biomarkers for population screening with those of the currently available tests for the detection of pregnancies at risk for fetal anuploidies. Thus, the collection of biological samples, along with clinical, ultrasonographic and biochemical data in large biobanks, is becoming a necessity. Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

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