Proteomic and metabolomic approaches to the study of polycystic ovary syndrome

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Contents lists available at SciVerse ScienceDirect

Molecular and Cellular Endocrinology journal homepage: www.elsevier.com/locate/mce

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Review

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Proteomic and metabolomic approaches to the study of polycystic ovary syndrome

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Q1

María Insenser, Rafael Montes-Nieto, Mora Murri, Héctor F. Escobar-Morreale ⇑

Q2

Diabetes, Obesity and Human Reproduction Research Group, Hospital Universitario Ramón y Cajal, Universidad de Alcalá, E-28034 Madrid, Spain Instituto Ramón y Cajal de Investigación Sanitaria IRYCIS, E-28034 Madrid, Spain Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), E-28034 Madrid, Spain

a r t i c l e

i n f o

Article history: Received 16 December 2012 Received in revised form 5 February 2013 Accepted 11 February 2013 Available online xxxx Keywords: Androgen excess Hyperandrogenism Nontargeted Polycystic ovary syndrome proteomics Metabolomics

a b s t r a c t Polycystic ovary syndrome (PCOS) is considered a complex multifactorial disorder resulting from the interaction of genetic, environmental, and lifestyle influences. Nontargeted proteomics and metabolomics have been used in the past years with the aim of identifying molecules potentially involved in the pathophysiology of this frequent disorder. The biomolecules identified so far participate in many metabolic pathways, including energy metabolism (glucose and lipid metabolism), protein metabolic processes and protein folding, cytoskeleton structure, immune response, inflammation and iron metabolism, fibrinolysis and thrombosis, oxidative stress and intracellular calcium metabolism. These molecules provide key information about molecular functions altered in PCOS and raise questions concerning their precise role in the pathogenesis of this syndrome. The biomolecules identified by nontargeted proteomic and metabolomic approaches should be considered as candidates in future studies aiming to define specific molecular phenotypes of PCOS. Ó 2013 Published by Elsevier Ireland Ltd.

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Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proteomic and metabolomic techniques and their application to the study of PCOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biomolecules related to energy metabolic pathways. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Carbohydrate metabolism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Lipid metabolism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Biomolecules related to protein metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Biomolecules related to protein folding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Proteins related to cytoskeleton structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Biomolecules related to immune response, inflammation and iron metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Biomolecules related to regulation of fibrinolysis and thrombosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. Biomolecules related to oxidative stress. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10. Biomolecules related to calcium metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11. Biomolecules with other functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12. Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations: PCOS, polycystic ovary syndrome. ⇑ Corresponding author. Address: Department of Endocrinology, Hospital Ramón y Cajal & University of Alcalá, Carretera de Colmenar, km 9’1, E-28034 Madrid, Spain. Tel./fax: +34 91 3369029. E-mail address: [email protected] (H.F. Escobar-Morreale).

1. Introduction

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Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders, affecting approximately 6–7% of women of childbearing age (Asuncion et al., 2000; Azziz et al., 2004; Carmina and Lobo, 1999; Moran et al., 2010; Sanchon et al., 2012). The diagnosis of PCOS relies on the association of clinical and/or

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0303-7207/$ - see front matter Ó 2013 Published by Elsevier Ireland Ltd. http://dx.doi.org/10.1016/j.mce.2013.02.009

Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009

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biochemical hyperandrogenism with chronic oligo-anovulation and/or polycystic ovarian morphology, after the exclusion of specific etiologies (Azziz et al., 2006). Aside from symptoms of androgen excess and reproductive consequences, PCOS associates long-term risk factors for the development of severe metabolic disorders including obesity, diabetes and cardiovascular disease (Alvarez-Blasco et al., 2006; Escobar-Morreale and San Millan, 2007; Wild et al., 2010). The pathogenesis of PCOS is complex and its etiology remains unclear. Androgen excess is a primary defect leading to PCOS (Wickenheisser et al., 2006) and, accordingly, PCOS is currently considered a mainly hyperandrogenic disorder (Azziz et al., 2006). Many patients with PCOS are overweight or obese (Carmina et al., 2007; Gambineri et al., 2002) and a predominantly abdominal distribution of body fat is particularly frequent in these women (Borruel et al., in press; Escobar-Morreale and San Millan, 2007). Moreover, insulin resistance is common in both obese and lean women with PCOS (Escobar-Morreale and San Millan, 2007) and 50–70% of patients with PCOS have insulin resistance and compensatory hyperinsulinism (Gambineri et al., 2002). To explain the association of androgen excess, abdominal adiposity, insulin resistance and metabolic derangements in women with PCOS, we proposed the existence of a vicious circle in these women, that may start during early stages of life or even prenatally, whereby androgen excess favoring the abdominal deposition of fat further facilitates androgen secretion by the ovaries and adrenals in PCOS patients (Fig. 1) (Escobar-Morreale and San Millan, 2007). The facilitation of androgen secretion by abdominal adiposity is mediated through the effects of several mediators secreted by adipose tissue that may favor androgen secretion directly or through the induction of insulin resistance and compensatory hyperinsulinism (Nahum et al., 1995; Tosi et al., 2009). Heterogeneity is an intrinsic characteristic of PCOS, from its definition to its phenotype, and is also present in the mechanisms leading to its development: the relative contributions of androgen excess and other factors to the development of PCOS in the individual patient are also heterogeneous. We explain PCOS as the result

of a primary defect in steroidogenesis that leads to androgen excess of variable severity (Fig. 2) (Escobar-Morreale and San Millan, 2007). In some patients the defect is severe enough to result in PCOS without the participation of any other contributing factor. In others, a very mild defect in steroidogenesis is triggered by obesity, abdominal adiposity and insulin resistance, and the complete PCOS phenotype only develops when these factors are present. Between the two extremes there is a spectrum in the severity of the defect in androgen secretion, explaining the heterogeneity in the relative contribution of obesity and insulin resistance to the PCOS phenotype. From an evolutionary perspective PCOS appears to be the result of the interaction of predisposing and protective genetic variants that may have provided survival advantage during times of stress and famine, with environmental factors such as life-style, diet and exercise that are heavily dependent on ethnicity (EscobarMorreale et al., 2005a). However, our current understanding of the genetic, molecular and cellular mechanisms underlying PCOS is quite limited, in spite of considerable research efforts (EscobarMorreale et al., 2005a; Simoni et al., 2008). The fact that most of this unsuccessful research efforts targeted specific proteins and genes based on previous knowledge about their role in certain disorders and metabolic and signaling pathways related to PCOS prompted a shift towards the application of nontargeted approaches to the study of this prevalent disorder. The development and implementation of nontargeted highthroughput technologies, namely omics, might identify novel candidate molecules, which whose participation in the pathogenesis of PCOS could have not been suspected because of previous knowledge on the issue. Identification of such molecules, aside from providing new insights on PCOS, may even lead to the development of more precise diagnostic techniques and the identification of new therapeutic targets. The contributions of genomics (genome-wide association studies) and transcriptomics (gene arrays) to the study of PCOS have been reviewed recently in this journal (Kosova and Urbanek, 2013). Therefore, the present review focuses on recent studies that

Fig. 1. Polycystic ovary syndrome and abdominal adiposity as the result of a vicious circle represented by the black arrows: androgen excess favors the abdominal deposition of body fat, and visceral fat facilitates androgen excess of ovarian and/or adrenal origin by the direct effects (white arrow) of several autocrine, paracrine and endocrine mediators, or indirectly by the induction of insulin resistance and hyperinsulinism. Reproduced from Escobar-Morreale and San-Millán (Escobar-Morreale and San Millan, 2007), with permission. Copyright Elsevier, 2007.

Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009

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Fig. 2. The polycystic ovary syndrome is the result of the interaction of a primary abnormality in androgen secretion leading to androgen excess, with environmental factors such as abdominal adiposity, obesity and insulin resistance. In one extreme (⁄), some patients the disorder is severe enough to result in PCOS even in the absence of triggering environmental factors. In the other extreme ( ), a very mild defect in androgen secretion is amplified by the coexistence of abdominal adiposity, obesity and/or insulin resistance. Between the two extremes, there is a spectrum in the severity of the primary defect in androgen secretion, explaining the heterogeneity of PCOS patients with regards to the presence of obesity and metabolic comorbidities. Yet all patients share a primary defect in androgen secretion. Reproduced from Escobar-Morreale and SanMillán (Escobar-Morreale and San Millan, 2007), with permission. Copyright Elsevier, 2007.

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applied different proteomic and metabolomic platforms to biological fluids and tissues from patients with PCOS and aims to describe the proteins, metabolites and metabolic pathways associated with PCOS according to these approaches.

2. Proteomic and metabolomic techniques and their application to the study of PCOS Proteomics and metabolomics (metabonomics) analyze the last steps of the flow of molecular information that begins with the transcription of genes into mRNA, processes studied by genomics and transcriptomics (Fig. 3). Among omics techniques, proteomics and metabolomics have a high potential of linking genotype and phenotype, because proteins and metabolites, rather than nucleic acids, represent the functional output of a cell. Proteomic and metabolomic approaches allow the nontargeted identification and characterization of the complete collection of proteins or metabolites present in a cell or tissue under particular conditions. A similar term, metabonomics, is usually restricted to studies of human nutrition and responses to drugs or disease, and usually compares profiles without identifying individual compounds. The integration of data-dense information from the different omics platforms may provide a global vision and understanding of biological systems on a comprehensive scale. Different proteomic and metabolomic platforms have been used in the past years to collect data from the blood and other target organs and tissues – such as the ovary and visceral adipose tissue – from patients with PCOS, allowing the nontargeted discovery of novel molecules and metabolic pathways associated with this complex metabolic disorder. We conducted a search of the literature using the Entrez-PubMed online facility. Studies containing original data on biomolecules identified by proteomic or metabolomic approaches in women with

PCOS and controls published before July 2012 were included. Search terms were ‘‘polycystic ovary syndrome OR PCOS’’ and ‘‘proteomic’’, ‘‘proteomics’’, ‘‘metabolomics’’ or ‘‘metabonomics’’ without any limits/restrictions. In addition, a hand search of the references of the retrieved articles and relevant reviews was performed to identify other potentially eligible studies. Table 1 summarizes the studies that compared samples from women with PCOS with samples from women without androgen excess, using nontargeted proteomic or metabolomic approaches. Different samples have been used including serum, plasma, visceral adipose and ovarian tissue, ovarian granulosa cells and peripheral T lymphocytes. Each type of sample has its own advantages and limitations for proteomic and metabolomic analysis. The study of tissues is more likely to show differences between affected and control patients that truly relate to the pathogenesis of PCOS. However five of the studies – including the two that used metabolomic approaches – used serum or plasma, possibly because these samples are easy to obtain and the biomolecules secreted into body fluids during or after disease may reflect a broad range of pathophysiological conditions. Most of the proteomic studies applied gel-based techniques. Four studies applied two-dimensional electrophoresis (2D-PAGE), separating proteins by their molecular weight and isoelectric point in an acrilamide gel matrix, using traditional stains such as silver or coomasie blue (Borro et al., 2007; Choi et al., 2010; Ma et al., 2007; Misiti et al., 2010) to visualize the proteins. Two studies (Corton et al., 2008; Insenser et al., 2010) used two-dimensional differential in-gel electrophoresis (DIGEs), a state-of-the-art technique that uses fluorescent dyes to stain the samples before electrophoresis allowing a higher reproducibility than traditional staining techniques, and that permits the quantification of protein abundance with higher accuracy and statistical certainty (Unlu et al., 1997). Another study (Matharoo-Ball et al., 2007) analyzed serum samples using 2D-PAGE and also using reversed-phase

Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009

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Fig. 3. Steps in the flow of molecular information that begins the transcription of genes into mRNA, follows with protein synthesis and post-translational modification and ends in the synthesis of metabolites, and the omics sciences that study each of these steps.

Table 1 Summary of published studies comparing samples from women with polycystic ovary syndrome with samples from healthy control women using nontargeted proteomic or metabolomic approaches. Author, Year

Biological sample

Sample size

Analytic method

Identification method

Analysis software

Proteins/ metabolites showing different abundance

Ma et al. (2007)

Ovarian tissue

2D-PAGE,silver stain

Serum

MALDI-TOFTOF MALDI-TOF

ImageMasterTM 2D Platinum Delta 2D

69 (54;, 15")

Matharoo-Ball et al. (2007) Zhao et al. (2007)

Serum

Biomarker Wizard

17 (16;, 1")

Borro et al. (2007) Misiti et al. (2010) Corton et al. (2008)

Peripheral T cells

PCOS = 3 Control = 3 PCOS = 5, 6 and 12 Control = 5, 6 and 12 PCOS = 30 Control = 30 PCOS = 10 Control = 10 PCOS = 10 Control = 9 PCOS = 12 Control = 12 PCOS = 14 Control = 11 PCOS = 39 Control = 36 PCOS = 36 Control = 34

Insenser et al. (2010) Choi et al. (2010) Escobar-Morreale et al. (2012) Sun et al. (2012)

Visceral Adipose tissue Plasma Ovarian granulosa cells Plasma Plasma

2D-PAGE/RP-SPE

SELDI-TOF

4 (3;, 1")

2D-PAGE, coomassie blue 2D-DIGE

MALDI-TOF

PDQuest

11 (8;, 3")

MALDI-TOF

Decyder

8 (6;, 2")

2D-DIGE

MALDI-TOF

Decyder

4 (2;, 2")

2D-PAGE, silver stain

LC–MS/MS

5 (5;, 10")

Non targeted GC–MS

GC–MS

ImageMasterTM 2D Platinum XCMS Matlab V NMR Matlab

14 (10;, 4")

1H-NMR

11 (8;, 3")

Abbreviations ", increased; ;, decreased; 1H-NMR, proton nuclear magnetic resonance; 2D-DIGE, two-dimensional differential-in-gel electrophoresis; 2D-PAGE/RP-SPE, twodimensional gel electrophoresis/reversed-phase solid-phase-extraction; GC–MS, gas chromatography–mass spectrometry; LC–MS/MS, liquid chromatography–mass spectrometry; MALDI-TOF, matrix-assisted laser desorption/ionization-time-of-fight; SELDI-TOF, surface-enhanced laser desorption/ionization time-of-fight.

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solid-phase-extraction (RP-SPE) coupled with SDS–PAGE. RP-SPE allows fractionation of the sample before SDS–PAGE increasing resolution. Proteins showing differential abundance in gel detected are excised and identified using mass spectrometry (MS). Most of these studies used MALDI-TOF for protein identification (Borro et al., 2007; Corton et al., 2008; Insenser et al., 2010; Ma et al., 2007; Matharoo-Ball et al., 2007; Misiti et al., 2010), with the exception of one study that applied LC–MS (Choi et al., 2010). Only one study used a gel-free technique, surface-enhanced laser adsorption/ionization-time of flight (SELDI-TOF), where protein mix of the samples are place upon a chip and are directly analyzed by mass spectrometry (Zhao et al., 2007).

Metabolomic studies were performed analyzing plasma samples using nuclear magnetic resonance (NMR) (Sun et al., 2012) or gas chromatography coupled with mass spectrometry (GC– MS) systems (Escobar-Morreale et al., 2012). NMR is based on the analysis of the signal that each molecule emits when their nuclei spins orientations are modified after an exposure to a magnetic field and radiofrequencies (Pan and Raftery, 2007). This signal is translated into a spectrum that is characteristic for each molecule and can be matched with those of existing databases. GC–MS separates molecules that can be vaporized without chemical decomposition from a mixture being further analyzed by the coupled MS equipment (Mitrevski et al., 2009). GC–MS and NMR have dem-

Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009

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onstrated to be complementary analytical techniques in metabolomics-based studies (Lenz et al., 2004; Sieber et al., 2009; Williams et al., 2006). The advantages and disadvantages of each analytical technique and their current status in the field of metabolomics have been reviewed elsewhere (Dunn et al., 2005; Lenz and Wilson, 2007; Moco et al., 2009). Table 2 shows a summary of biomolecules present in different abundances when comparing samples from women with PCOS with those obtained from healthy control women using proteomic or metabolomic approaches. In the following sections we will describe several of these molecules grouped according to the main biological process in which they participate. Of note, some of the proteins may be included in more than one group, because of their participation in several biological processes.

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3. Biomolecules related to energy metabolic pathways

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As discussed above, disorders of energy metabolism such as obesity or insulin resistance are frequent in women with PCOS (Escobar-Morreale and San Millan, 2007). Therefore, it is not surprising that both proteomics and metabolomics have identified differences in molecules involved in these processes.

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3.1. Carbohydrate metabolism

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Proteomic studies show modulation of several proteins related to carbohydrate metabolism. The abundance of several proteins such as aconitate hydratase, fructose-bisphosphate aldolase A, malate dehydrogenase, isozymes M1/M2 of pyruvate kinase, and transaldolase has been found to be increased in ovarian tissue and in ovarian granulosa cells from PCOS patients, whereas the abundance of UDP-glucose 6-dehydrogenase protein was reduced (Ma et al., 2007). UDP-glucose 6-dehydrogenase is involved in the biosynthesis of glycosaminglycans and its expression is stimulated in cultured mammary and prostate human cells by androgens (Lapointe and Labrie, 1999; Nelson et al., 2002), where UDP-glucose 6-dehydrogenase may play a role in limiting intracellular androgen availability (Wei et al., 2009). The abundance of a-enolase is decreased in T lymphocytes (Misiti et al., 2010) and that of triosephosphate isomerase is decreased in visceral adipose tissue (Corton et al., 2008) from patients with PCOS. Enolase is a glycolitic enzyme involved in many processes, including growth control, hypoxia tolerance and allergic responses, whereas triosephosphate isomerase may contribute to cytoskeleton dysregulation by interfering with several functions of adipose tissue cells (Corton et al., 2008). Of note, cDNA microarrays found increased gene expression of triosephosphate isomerase in ovarian tissue from patients with PCOS (Diao et al., 2004). An NMR-based metabolomic approach showed a mild but statistically significant increase in plasma lactic acid concentrations accompanied by a minor decrease in glucose levels in non-obese women with PCOS, suggesting disturbed glucose metabolism with increased glycolytic activity in these women. Plasma citric acid concentrations were significantly decreased in patients with PCOS, indicating impairment of the tricarboxylic acid cycle (Sun et al., 2012). GC–MS-based metabolomics confirmed the increase in lactic acid in non-obese patients with PCOS, yet lactic acid was actually reduced in obese patients suggesting increased insulinstimulated glucose uptake and consumption in the muscle of non-obese patients but not in obese women with PCOS (EscobarMorreale et al., 2012). This apparent discrepancy may indicate that not all tissues of non-obese patients with PCOS are insulin resistant, whereas the extent of insulin resistance is broader in obese

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patients and dominates the metabolic picture (Escobar-Morreale et al., 2012).

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3.2. Lipid metabolism

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Women with PCOS frequently associate an atherogenic serum lipid profile consisting of increased triglycerides, cholesterol, and low density lipoprotein cholesterol concentrations, and reduced apolipoprotein A-I levels (Holte et al., 1994; Legro et al., 1999; Valkenburg et al., 2008). Insulin resistance, androgen excess and obesity may contribute to the abnormalities of lipid metabolism observed in women with PCOS (Wild et al., 2010). Apolipoprotein A-1 (ApoA-I), the major structural protein component of HDL-cholesterol particles, has pleiotropic biological functions that include the promotion of macrophage cholesterol efflux and the stimulation of reverse lipid transport, inhibiting LDL oxidation and scavenging toxic phospholipids, and has anti-inflammatory properties (Shah, 2011). In conceptual agreement with previous reports (Holte et al., 1994; Legro et al., 1999; Valkenburg et al., 2008), proteomic techniques found a decreased abundance of ApoA-I in visceral adipose tissue and in whole ovarian tissue from women with PCOS (Corton et al., 2008; Ma et al., 2007). Moreover, reduced ApoA-I abundance in granulosa cells from these women may influence the expression of steroidogenic enzymes and the production of the steroid hormone progesterone (Choi et al., 2010). Furthermore, a nontargeted NMR-based metabolomic approach confirmed increased levels of LDL-cholesterol and decreased levels of HDL-cholesterol in PCOS, as well as decreased phosphatidylcholine and increased plasma sphingomyelin (Sun et al., 2012) that may contribute to the increased cardiovascular risk disease of patients with PCOS (Wild et al., 2010). Apolipoprotein C-I (ApoC-I) inhibits lipoprotein metabolism in the liver, which prolongs the retention time of lipoprotein in blood circulation, and transforms it into more low-density lipoprotein, thereby increasing cardiovascular risk (Brewer, 1999). A SELDI protein chip showed increased serum ApoC-I levels in women with PCOS compared with control women, especially in those presenting with insulin resistance (Huang et al., 2010). Carnitine o-palmitoyltransferase I (CPT I) is the key enzyme in the carnitine-dependent transport across the mitochondrial inner membrane and its deficiency results in a decreased rate of fatty acid b-oxidation (McGarry and Brown, 1997). Proteomic techniques showed increased abundance of CPT I, possibly indicating increased fatty acid b-oxidation, in ovarian tissue from patients with PCOS (Ma et al., 2007). Adipocyte plasma membrane-associated protein (APMAP) exhibits strong arylesterase activity with b-naphthyl acetate and phenyl acetate and may play a role in adipocyte differentiation (Ilhan et al., 2008). APMAP may represent a novel member of paraoxonases (Ilhan et al., 2008), which are known to be involved in antioxidant processes. The decreased APMAP abundance found in visceral adipose tissue from patients with PCOS (Corton et al., 2008) might contribute to the impairment in antioxidant defense characteristic of PCOS (Murri et al., 2013). A GC–MS characterization of intermediate metabolism identified statistically significant interactions between PCOS and obesity in the abundance of several plasma metabolites, indicating that the metabolic derangements associated with PCOS are heavily influenced by the development of obesity (Escobar-Morreale et al., 2012). The increased concentrations of glycerol and free fatty acids such as oleic acid, linoleic acid and glyceric acid observed in obese patients with PCOS suggested increased lipolysis secondary to impaired insulin action in adipose tissue. However, the same metabolites were decreased in non-obese patients with PCOS, indicating conserved insulin action in the adipose tissue of these women (Escobar-Morreale et al., 2012). Together with the increase ob-

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Table 2 Summary of biomolecules showing differential abundance in women with polycystic ovary syndrome compared with healthy control women using proteomic or metabolomic approaches, grouped according to their main biological process. Accession numbera

Name

Clinical specimen

Abundanceb

ACO2 (22q13.2) ALDOA (16p11.2) MDH2 (7cen-q22)

Q99798 P04075 P40926

Ovarian tissue Ovarian tissue Ovarian tissue

" " "

PKM2 (15q22) TALDO1 (11p15.5-p15.4) UGDH (4p15.1)

P14618 P37837 O60701 HMDB00190

Aconitate hydratase, mitochondrial Fructose-bisphosphate aldolase A Malate dehydrogenase, mitochondrial Pyruvate kinase, isozymes M1/M2 Transaldolase UDP-glucose 6-dehydrogenase Lactic acid

Ovarian tissue Ovarian tissue Ovarian tissue Plasma

HMDB00094 P06733 P60174

Citrate a-enolase Triosephosphate isomerase

Plasma T Lymphocytes Visceral adipose tissue

" " ; " in non-obese, ; in obese " ; ; ;

al.

P50416 HMDB00067

Carnitine o-palmitoyltransferase I Cholesterol

Ovarian tissue Plasma

" ;

al.

HMDB00139

Glyceric acid

Plasma

al.

HMDB00448

Adipic acid

Plasma

al.

HMDB00131

Glycerol

Plasma

al.

HMDB00673

Linoleic acid

Plasma

al.

HMDB00207

Oleic acid

Plasma

P02654 Q9HDC9

Apolipoprotein C-I Adipocyte plasma membraneassociated protein Apolipoprotein A-1

Serum Visceral adipose tissue

; in non-obese, in obese ; in non-obese, in obese ; in non-obese, in obese ; in non-obese, in obese ; in non-obese, in obese " ;

Author, Year

Gene (Location)

Biomolecules related to energy Carbohydrate metabolism Ma et al. (2007) Ma et al. (2007) Ma et al. (2007)

metabolic pathways

Ma et al. (2007) Ma et al. (2007) Ma et al. (2007) Escobar-Morreale et al. (2012) Sun et al. (2012) Sun et al. (2012) Misiti et al. (2010) Corton et al. (2008) Lipid metabolism Ma et al. (2007) Escobar-Morreale et (2012) Escobar-Morreale et (2012) Escobar-Morreale et (2012) Escobar-Morreale et (2012) Escobar-Morreale et (2012) Escobar-Morreale et (2012) Zhao et al. (2007) Corton et al. (2008)

ENO1 (1p36.2) TPI1 (12p13)d CPT1A (11q13.2)

APOC1 (19q13.2) APMAP (20p11.2)

Ma et al. (2007) APOA1 (11q23-q24) Corton et al. (2008) Choi et al. (2010) Biomolecules related to protein metabolism Ma et al. (2007) ST3GAL3 (1p34.1)

P02647

Q11203

Ma et al. (2007) Ma et al. (2007) Ma et al. (2007)

PSMC6 (14q22.1) PSMC3 (11p11.2) PSMB8 (6p21.3)

P62333 P17980 P28062

Ma et al. (2007)

MAT2A (2p11.2)

P31153

Ma et al. (2007) Ma et al. (2007)

LAP3 (4p15.32) GRHPR (9q12)

P28838 Q9UBQ7

Ma et al. (2007) Sun et al. (2012) Sun et al. (2012) Sun et al. (2012) Sun et al. (2012) Escobar-Morreale et al. (2012) Escobar-Morreale et al. (2012) Misiti et al. (2010) Misiti et al. (2010)

MAT2B (5q34-q35)

Ovarian tissue Visceral adipose tissue Granulosa cells

;

Ovarian tissue

"

Ovarian tissue Ovarian tissue Ovarian tissue

" " "

Ovarian tissue

"

Ovarian tissue Ovarian tissue

" "

Q567T7 HMDB00641 HMDB00696 HMDB00517 HMDB00687 HMDB00161

CMP-N-acetylneuraminate-b-1,4galactoside a-2,3-sialyltransferase 26S protease regulatory subunit 10B 26S protease regulatory subunit 6A Proteasome subunit b type 8 precursor S-adenosylmethionine synthetase, c form Cytosol aminopeptidase Glyoxylate reductase/ hydroxypyruvate reductase Methionine adenosyltransferase II Glutamine Methionine Arginine Leucine Alanine

Ovarian tissue Plasma Plasma Plasma Plasma Plasma

; ; ; ; ; ;

HMDB00695

2-ketoisocaproic acid

Plasma

;

P07339 P31937

Cathepsin D 3-hydroxyisobutyrate dehydrogenase

T Lymphocytes T Lymphocytes

; ;

Biomolecules related to protein folding Choi et al. (2010) PRKCSH (19p13.2) Choi et al. (2010) HSP90B1 (2q24.2-q24.3) Choi et al. (2010) P4HB (17q25) Choi et al. (2010) HYOU1 (11q23.1-q23.3) Choi et al. (2010) HSPD1 (2q33.1)d Choi et al. (2010) PDIA6 (2p25.1) Ma et al. (2007) SERPINH1 (11q13.5) Ma et al. (2007) CCT8 (21q22.11) Ma et al. (2007) PPIA (7p13)

P14314 P14625 P07237 Q9Y4L1 P10809 Q15084 P50454 P50990 P62937

Granulosa cells Granulosa cells Granulosa cells Granulosa cells Granulosa cells Granulosa cells Ovarian tissue Ovarian tissue Ovarian tissue

" " " " ; ; " " "

Ma et al. (2007) Ma et al. (2007)

P04792 P61604

Glucosidase 2, b subunit Heat shock protein 90 kDa Protein disulfide-isomerase Hypoxia up-regulated protein 1 Heat shock protein 60 kDa Protein disulfide isomerase A6 Heat shock protein 47 kDa T-complex protein 1, h subunit Peptidyl-prolyl cis–trans isomerase A, Cyclophilin A Heat shock 27 kDa Heat shock protein 10 kDa

Ovarian tissue Ovarian tissue

; ;

CTSD (11p15.5) HIBADH (7p15.2)

HSPB1 (7q11.23) HSPE1 (2q33.1)

" = " " "

Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009

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M. Insenser et al. / Molecular and Cellular Endocrinology xxx (2013) xxx–xxx Table 2 (continued) Author, Year

Gene (Location)

Accession numbera

Name

Clinical specimen

Abundanceb

Misiti et al. (2010)

PDIA3 (15q15)

P30101

Protein disulphide-isomerase A3

T Lymphocytes

;

Biomolecules related to cytoskeleton structure Choi et al. (2010) TUBB (6p21.33) Choi et al. (2010) TUBA1A (12q13.12) Ma et al. (2007) ACTR1B (2q11.1-q11.2) Ma et al. (2007) CCT3 (1q23) Ma et al. (2007) COL2A1 (12q13.11) Ma et al. (2007) DPYSL2 (8p22-p21)

P07437 Q71U36 P42025 Q5SZY0 P02458 Q16555

Granulosa cells Granulosa cells Ovarian tissue Ovarian tissue Ovarian tissue Ovarian tissue

" ; " " " "

Ma et al. (2007)

DPYSL3 (5q32)

Q14195

Ovarian tissue

"

Ma Ma Ma Ma

LMNA (1q22)d VIM (10p13) LMNB2 (19p13.3)c

P02545 P08670 Q03252 Q548L2

Ovarian Ovarian Ovarian Ovarian

tissue tissue tissue tissue

" " " "

NME2 (17q21.3) CAPG (2p11.2) FSCN1 (7p22) TAGLN (11q23.2) ARHGDIA (17q25.3)d CAPZA1 (1p13.2) CFL1 (11q13) ACTB (7p22)

P22392 P40121 Q16658 Q01995 P52565 P52907 P23528 P60709

b-tubulin a-tubulin b-centractin T-complex protein 1, c subunit Collagen a-2(I) chain precursor Dihydropyrimidinase-related protein 2 Dihydropyrimidinase-related protein 3 Lamin A/C Vimentin Lamin B2 CTCL tumor antigen HD-CL-06 (Vimentin variant) Nucleoside diphosphate kinase B Macrophage capping protein Fascin Transgelin Rho GDP-dissociation inhibitor 1 F-actin capping protein, a-1 subunit Cofilin-1 Actin b

Ovarian tissue Ovarian tissue Ovarian tissue Ovarian tissue T Lymphocytes T Lymphocytes T Lymphocytes Visceral adipose tissue

" ; ; ; ; ; ; ;

Complement C3 precursor Transferrin Haptoglobin a and b chain

Ovarian tissue Plasma Plasma Plasma Plasma Plasma Serum

" " " ; ; " "

Haptoglobin b chain Complement C4a3c chain Complement C4a4c chain Complement C4c chain Raf kinase inhibitor protein Albumin

Plasma Serum Serum Serum T Lymphocytes Visceral adipose tissue

; " " ; ; ;

Antithrombin-III Fibrinogen, a chain Fibrinogen, c chain Plasminogen-related protein A Annexin A2 Platelet basic protein Annexin A5

Ovarian tissue Ovarian tissue Ovarian tissue Ovarian tissue Ovarian tissue T Lymphocytes Visceral adipose tissue Granulosa cells

" " " " ; " "

Glutathione S-transferase M2 NG,NG-dimethylarginine dimethylaminohydrolase 1 Superoxide dismutase Peroxiredoxin 1 Glutathione S-transferase M3 Peroxiredoxin 2

Ovarian tissue Ovarian tissue

" "

T Lymphocytes T Lymphocytes Visceral adipose tissue Visceral adipose tissue

" " " ;

Calreticulin Reticulocalbin 1 precursor Annexin A6 Osteoglycin Calcium/calmodulin-dependent protein kinase type II Annexin A11

Granulosa cells Granulosa cells Granulosa cells Ovarian tissue Ovarian tissue

" " ; " "

Ovarian tissue

;

ATP synthase-coupling factor 6, mitochondrial Translation initiation factor eIF-2B, c subunit Dehydrogenase/reductase SDR family member 4

Granulosa cells

"

Ovarian tissue

"

Ovarian tissue

"

et et et et

al. al. al. al.

(2007) (2007) (2007) (2007)

Ma et al. (2007) Ma et al. (2007) Ma et al. (2007) Ma et al. (2007) Misiti et al. (2010) Misiti et al. (2010) Misiti et al. (2010) Corton et al. (2008)

Biomolecules related to immune response, inflammation and iron metabolism Ma et al. (2007) FKBP3 (14q21.2) Q00688 Sun et al. (2012) ORM1 (9q31-q32) P02763 Insenser et al. (2010) IGKC (2p12)d P01834 Insenser et al. (2010) A2 M (12p13.31) P01023 Insenser et al. (2010) C3 (19p13.3-p13.2)c,d P01024 Insenser et al. (2010) TF (3q22.1) P02787 Matharoo-Ball et al. (2007) HP (16q22.2) P00738 Insenser et al. (2010) Matharoo-Ball et al. (2007) Matharoo-Ball et al. (2007) Matharoo-Ball et al. (2007) Misiti et al. (2010) Corton et al. (2008)

C4A (6p21.3) C4A (6p21.3) C4B (6p21.3) RAF1 (3p25)d ALB (4q13.3)

P0C0L4 P0C0L5 P0C0L5 P04049 P02768

Biomolecules related to regulation of fibrinolisis and thrombosis Ma et al. (2007) SERPINC1 (1q25.1) P01008 Ma et al. (2007) FGA (4q28) P02671 Ma et al. (2007) FGG (4q28) P02679 Ma et al. (2007) PLGLA (2q12.2) Q15195 Ma et al. (2007) ANXA2 (15q22.2) P07355 d Misiti et al. (2010) PPBP (4q12-q13) P02775 Corton et al. (2008), Choi ANXA5 (4q27) P08758 et al. (2010) Biomolecules related to oxidative stress Ma et al. (2007) GSTM2 (1p13.3) Ma et al. (2007) DDAH1 (1p22)

P28161 O94760

Misiti et al. (2010) Misiti et al. (2010) Corton et al. (2008) Corton et al. (2008)

P04179 Q06830 P21266 P32119

SOD2 (6q25.3) PRDX1 (1p34.1) GSTM3 (1p13.3)d PRDX2 (19p13.2)d

Biomoleculess related to calcium metabolism Choi et al. (2010) CALR (19p13.3-p13.2) Choi et al. (2010) RCN1 (11p13) Choi et al. (2010) ANXA6 (5q33.1) Ma et al. (2007) OGN (9q22)d Ma et al. (2007) CAMK2G (10q22) Ma et al. (2007)

ANXA11 (10q23)

c,d

P27797 Q15293 P08133 P20774 Q13555 P50995

Biomolecules with other functions Choi et al. (2010) ATP5 J (21q21.1)d

P18859

Ma et al. (2007)

EIF2B3 (1p34.1)

Q9NR50

Ma et al. (2007)

DHRS4 (14q11.2)

Q9BTZ2

Peptidyl-prolyl cis–trans isomerase

a1-acid glycoprotein j free light chain region a2-macroglobulin

(continued on next page)

Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009

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Table 2 (continued) Author, Year

Gene (Location)

Accession numbera

Name

Clinical specimen

Abundanceb

Ma et al. (2007)

EDARADD (1q42.3)

Q8WWZ3

Ectodysplasin A receptor-associated adapter protein Far upstream element binding protein 1 Far upstream element binding protein 2 Flotillin-1 GTP:AMP phosphotransferase, mitochondrial Guanine nucleotide-binding protein G(I)/G(S)/G(T) Heterogeneous nuclear ribonucleoprotein A3, isoform a Heterogeneous nuclear ribonucleoproteins A2/B1 NSFL1 cofactor p47 Olfactomedin-like protein 3 Phosphatidylethanolamine-binding protein Proliferation-associated protein 2G4 SDF2 protein, stromal cell-derived factor 2 Signal recognition particle 9 kDa protein Structural maintenance of chromosome 3 Voltage-dependent anion-selective channel protein 2 Zinc finger FYVE domain-containing protein 19 5’(3’)-deoxyribonucleotidase, cytosolic type Heterogeneous nuclear ribonucleoprotein A1 High-affinity cGMP-specific 30,50cyclic phosphodiesterase 9A NADH-ubiquinone oxidoreductase 42 kDa Stromal cell-derived growth factor SF20 Creatine Dimethylamine a-tocopherol

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue Ovarian tissue

" "

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue Ovarian tissue Ovarian tissue

" " "

Ovarian tissue Ovarian tissue

" "

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue

"

Ovarian tissue

;

Ovarian tissue

;

Ovarian tissue

;

Ovarian tissue

;

Ovarian tissue

;

Plasma Plasma Plasma

" " ;

Plasma Plasma

; ;

Plasma Plasma

; ;

d

Q96AE4

Ma et al. (2007)

FUBP1 (1p31.1)

Ma et al. (2007)

KHSRP (19p13.3)c

Q92945

Ma et al. (2007) Ma et al. (2007)

FLOT1 (6p21.3) AK3 (9p24.1)

O75955 Q9UIJ7

Ma et al. (2007)

GNB1 (1p36.33)

P62873

Ma et al. (2007)

HNRNPA3 (2q31.2)

P51991

Ma et al. (2007)

HNRNPA2B1 (7p15)

P22626

Ma et al. (2007) Ma et al. (2007) Ma et al. (2007)

NSFL1C (20p13) OLFML3 (1p13.2) PEBP1 (12q24.23)

Q9UNZ2 Q9NRN5 P30086

Ma et al. (2007) Ma et al. (2007)

PA2G4 (12q13.2)c SDF2 (17q11.2)

Q9UQ80 Q6IBU4

Ma et al. (2007)

SRP9 (1q42.12)

P49458

Ma et al. (2007)

SMC3 (10q25)

Q9UQE7

Ma et al. (2007)

VDAC2 (10q22)

P45880

Ma et al. (2007)

ZFYVE19 (15q15.1)

Q96K21

Ma et al. (2007)

NT5C (17q25.1)

Q8TCD5

Ma et al. (2007)

HNRNPA1 (12q13.1)

P09651

Ma et al. (2007)

PDE9A (21q22.3)

O76083

Ma et al. (2007) Ma et al. (2007)

Q7Z518 C19orf10 (19p13.3)c

Q969H8

Sun et al. (2012) Sun et al. (2012) Escobar-Morreale et al. (2012) Sun et al. (2012) Sun et al. (2012)

HMDB00064 HMDB00087 HMDB01893

Sun et al. (2012) Sun et al. (2012)

HMDB00097 HMDB00562

HMDB00925 HMDB00086

Trimethylamine N-oxide Glycerophosphocholine/ phosphocholine Choline Creatinine

a

Accession numbers from SWISSPROT Database and Human Metabolome Database (HMDB). Biomolecules showing differences in abundance between patients with PCOS and non-hyperandrogenic controls: ", increased in PCOS; ;, decreased in PCOS. c Proteins that match with risk loci for PCOS identified by genome-wide association studies (Shi et al., 2012). d Proteins that match with genes identified by microarray techniques in PCOS (Corton et al., 2007; Chazenbalk et al., 2012; Diao et al., 2004; Jansen et al., 2004; Kaur et al., 2012; Kenigsberg et al., 2009; Savaris et al., 2011; Wood et al., 2005, 2007). b

354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369

served in lactic acid in non-obese women with PCOS mentioned earlier (Escobar-Morreale et al., 2012), which suggested conserved insulin action in muscle, these results suggest that several tissues and organs of non-obese patients with PCOS conserve insulin sensitivity, whereas insulin resistance dominates the metabolic picture in obese subjects with this syndrome (Escobar-Morreale et al., 2012). A targeted NMR-based metabolomic approach examined the effects of a moderate-intensity exercise program without weight loss on the circulating lipoprotein profile of women with PCOS suggesting that the analysis provided by NMR may be more sensitive indicators of the effect of exercise on lipoproteins than conventional biochemistry (Brown et al., 2009). Finally, application of a combination of LC–MS, GC–MS and NMR-based metabolomics to serum samples of women with PCOS obtained before and after 30 months of treatment with flutamide, metformin, pioglitazone and an oral

contraceptive pill, showed marked changes in the size of different lipoprotein particles, in conjunction with downstream metabolic oxidation products of LDL particles (Vinaixa et al., 2012).

370

4. Biomolecules related to protein metabolism

373

Amino acids are the building blocks of polypeptides and proteins serving as essential precursors for the synthesis of many essential molecules, and also regulate key metabolic pathways and processes that are vital to the health, growth, development, reproduction, and homeostasis of living organisms (Wu, 2009). GC–MS and NMR-based metabolomics found decreased plasma concentrations of several amino acids in patients with PCOS suggesting decreased transamination of leucine in the first catabolic step of branched-chain amino acids (BCAA) (Escobar-Morreale

374

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375 376 377 378 379 380 381 382

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et al., 2012; Sun et al., 2012). The peripheral insulin resistance characteristic of PCOS may account for these findings, because the utilization of BCAA in tissues such as muscle requires conserved insulin signaling. Furthermore, proteomics of T-cells showed reduced abundance of 3-hydroxyisobutyrate dehydrogenase, an enzyme implicated in BCAA catabolism (Misiti et al., 2010). Nontargeted proteomic techniques also indicated that ovarian tissue from patients with PCOS presented with increased abundance of proteins involved in the metabolism of amino acids (Sadenosylmethionine synthetase c form), in proteins associated with post-translational protein modification (CMP-N-acetylneuraminate-b-1,4-galactoside a-2,3-sialyltransferase) and in proteins involved in protein degradation (catepsin D, 26S protease regulatory subunit 10B, 26S protease regulatory subunit 6A and proteasome subunit b type 8 precursor) (Ma et al., 2007). Methionine adenosyltransferase II (MAT2B), and enzyme involved in the removal of homocystein, was decreased in the PCOS ovary (Ma et al., 2007). Increased plasma homocystein levels, a significant risk factor for cardiovascular disease, pre-eclampsia, and recurrent pregnancy loss, is characteristic of PCOS and is associated with insulin resistance (Schachter et al., 2003; Wijeyaratne et al., 2004). Cathepsin D is an acid protease active in intracellular protein breakdown involved in the pathogenesis of several diseases such as breast cancer and possibly Alzheimer disease (Bi et al., 2000; Vashishta et al., 2009). The biological significance of the decrease of this protein in observed in T lymphocytes from women with PCOS lacks a clear explanation (Misiti et al., 2010).

412

5. Biomolecules related to protein folding

413

The endoplasmic reticulum (ER) is an intracellular organelle involved in biosynthetic and secretory pathways. Recent studies have suggested that dysfunction of the ER is involved in human disease (Bando, 2012). The lumen of the ER is the major site for protein folding and contains molecular chaperones and folding enzymes. Only properly folded proteins are exported into the Golgi organelle, while incompletely folded proteins are retained within the ER to complete the folding process or are disposed into the cytosol to undergo degradation. Abnormalities in ER homeostasis due to increased protein synthesis, accumulation of misfolded proteins, or alterations in the calcium or redox balances lead to a condition termed ER stress (Ozcan and Tabas, 2012) that may also contribute to metabolic dysfunction in PCOS. An unbalance consisting of decreased PDIA6 and increased P4HB abundance was found in granulosa cells from patients with PCOS (Choi et al., 2010). These disulfide isomerases catalyze the rearrangement of disulfide bonds in proteins and may function as chaperones inhibiting the aggregation of misfolded proteins. Cyclophilin, that was overabundant in PCOS ovarian tissue (Ma et al., 2007), catalyzes the cis–trans isomerization of proline imidic peptide bonds in oligopeptides and accelerate the folding of proteins. Heat shock proteins (HSPs) are a highly conserved family of molecules involved in protein folding. Many components of survival and apoptotic pathways are regulated by molecular chaperones such as heat shock proteins (Fan et al., 2009). Hsp10 has only been considered a partner of Hsp60 in the Hsp60/10 protein folding machine. However, recent data suggested that Hsp10 may also be an inhibition of apoptosis and in follicular development and maturation (Fan et al., 2009). Hsp27 has been shown to play an important role in a variety of physiological processes including protein chaperoning, steroidogenesis and especially protection against apoptosis. The decreased in the protein abundance

414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445

9

of Hsp10, Hsp27 and Hsp60 in ovarian tissue and granulosa cells from patients with PCOS (Choi et al., 2010; Ma et al., 2007) might contribute to apoptosis within the ovarian follicle. In accordance, Hsp60 is down-regulated in adipose tissue in PCOS gene expression studies (Chazenbalk et al., 2012). Hypoxia up-regulated protein 1 has a pivotal role in cytoprotective cellular mechanisms triggered by oxygen deprivation and may play a role as a molecular chaperone participating in protein folding (Bando, 2012). The abundance of hypoxia up-regulated protein 1 is increased in granulosa cells from patients with PCOS (Choi et al., 2010), and possibly constitutes a cytoprotective mechanism against ER stress (Bando, 2012).

446

6. Proteins related to cytoskeleton structure

458

The involvement of cytoskeleton proteins in the pathogenesis of PCOS was initially suggested by genomic studies (Corton et al., 2007) and is also supported by nontargeted proteomic approaches. The actin cytoskeleton influences multiple intracellular membrane trafficking events including vesicle formation, exocytosis and endocytosis (Kanzaki, 2006). Ovarian tissue and granulosa cells from women with PCOS show an increase in the abundance of several cytoskeleton proteins including b-tubulin, b-centractin, T-complex protein 1 (c-subunit), nucleoside diphosphate kinase B, collagen a-2(I) chain precursor, dihydropyrimidinase-related protein 2 and 3, Lamin A/C and B2, vimentin, and CTCL tumor antigen HD-CL-06 (vimentin variant) (Choi et al., 2010; Ma et al., 2007). Of these molecules, the gene location of lamin B2 also overlaps with the PCOS risk loci identified by genome-wide association studies (Shi et al., 2012). Other proteins such as a-tubulin, macrophage capping protein, fascin, and transgelin were less abundant in tissues from PCOS patients (Choi et al., 2010; Ma et al., 2007). Actin plays an important role in regulating the insulin-mediated traffic of glucose transporter 4 (GLUT4) vesicles in the adipocyte plasma membrane; therefore the actin downregulation found in visceral adipose tissue from patients with PCOS might collaborate in reduced glucose transport and insulin resistance (Corton et al., 2008). Rho GTPases regulate the actin cytoskeleton and are involved in the translocation and fusion of GLUT4 vesicles to the plasma membrane. Nontargeted proteomics studies have identified differences between patients with PCOS and controls in two regulatory proteins of the Rho family. The abundance of Rho GDP-dissociation inhibitor 1 protein, which regulates the GDP/ GTP exchange of Rho proteins, was decreased in T lymphocytes (Misiti et al., 2010), whereas the abundance of nucleoside diphosphate kinase B, a negative regulator of Rho activity, was increased in ovarian tissue (Ma et al., 2007). However, Rho GDP-dissociation inhibitor 1 gene was up-regulated in oocytes in gene expression studies (Wood et al., 2007). Moreover, the abundances of cofilin1 and F-actin capping protein a-1 subunit were decreased in T lymphocytes from patients with PCOS (Misiti et al., 2010). Because rearrangement of the cytoskeleton is an essential step in T cell activation, changes in abundance of the above mentioned proteins might indicate dysfunction of the immune cellular system in PCOS (Borro et al., 2007).

459

7. Biomolecules related to immune response, inflammation and iron metabolism

499

Low-grade chronic inflammation emerged as a key contributor to the pathogenesis of PCOS (Escobar-Morreale et al., 2005b; Repaci et al., 2010), and could be considered one of the potential links between PCOS and long-term metabolic and cardiovascular complications (Wild et al., 2010).

501

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Most of the proteins showing differences in abundance in plasma and serum from patients with PCOS and appropriate controls were acute-phase proteins. Acute-phase proteins are those showing 25% or greater increments (positive acute-phase protein) or decreases (negative acute-phase protein) in their plasma concentration during acute inflammatory disorders (Gabay and Kushner, 1999). In conceptual agreement with the association of PCOS with chronic inflammation, the plasma abundance of positive acutephase proteins such as two members of the complement system and a1-acid glycoprotein (Matharoo-Ball et al., 2007; Sun et al., 2012) increased in patients with PCOS whereas the abundance of albumin, a negative acute-phase protein, was reduced in the visceral adipose tissue of these patients (Corton et al., 2008). However, other positive acute phase proteins such as a2 macroglobulin and C3 precursor were decreased in plasma from patients with PCOS (Insenser et al., 2010) but these differences were observed in two protein spots corresponding to minor protein species, whereas no changes were found in other spots corresponding to the same proteins. This finding suggests that post-translational modification of these proteins might occur in inflammatory disorders. The involvement of some of these proteins has also bee suggested by genomic and transcriptomic techniques. The gene location of complement C3 precursor overlaps with the loci identified by PCOS genomewide association studies and this gene is among those identified by microarray analyses (Kenigsberg et al., 2009; Shi et al., 2012). Furthermore, PCOS microarrays analyses showed down-regulation of the genes encoding j free light chain region and raf kinase inhibitor protein in adipose tissue and cumulus cells, respectively (Corton et al., 2007; Kenigsberg et al., 2009). The plasma abundance of transferring, a negative acute-phase protein, was increased in women with PCOS (Insenser et al., 2010). Even when this finding may weight against a role of inflammation as a contributor to PCOS metabolic dysfunction, it must be noted that transferrin is the major iron transporter in the circulation, and its increase in PCOS may not be related to inflammation but may represent a compensatory mechanism against the limitation of iron availability for erythropoiesis characteristic of chronic disorders (Insenser et al., 2010). Similarly, the decrease in a2 macroglobulin observed in patients with PCOS might be related to the increased body iron stores observed in these women (Escobar-Morreale, 2012; Luque-Ramirez et al., 2011). a2 macroglobulin is the blood carrier of hepcidin, which is the main negative signal for intestinal iron absorption. Decreased hepcidin concentrations may lead to increased iron absorption and mild iron overload in women with PCOS (Escobar-Morreale, 2012; Luque-Ramirez et al., 2011). Several acute-phase proteins such as those of the classic complement system initiate or sustain inflammation whereas haptoglobin, which has been identified in two nontargeted proteomic studies (Insenser et al., 2010; Matharoo-Ball et al., 2007) has anti-inflammatory properties and protects against oxidative stress by binding highly toxic ferric ions after processes of intravascular hemolysis. Application of 2D-DIGE identified reduced abundance of two protein species of haptoblobin b chain in plasma of patients with PCOS, and this finding was confirmed by Western blot (Insenser et al., 2010). On the contrary, SDS–PAGE analysis of the serum fractions obtained by RP-SPE identified increased abundance of a and b chains of haptoglobin in PCOS samples (Matharoo-Ball et al., 2007). Furthermore, as assayed by nephelometry, serum haptoglobin concentrations were not abnormal in PCOS, yet this disorder was associated with the Hp2 alleles of the gene encoding the a chain of haptoglobin, and this allele reduces the functional properties of haptoglobin (Alvarez-Blasco et al., 2009). These apparently discrepant results suggest that mutations and post-translational modifications of haptoglobin might reduce its anti-inflammatory properties in patients with PCOS without inducing major changes in its circulating concentrations.

The abundance of Raf kinase inhibitor protein (RKIP) was decreased in T lymphocytes of PCOS patients (Misiti et al., 2010). RKIP is an endogenous modulator of the function of multiple proteins, including Raf-1 kinase and kinases involved in the activation of the transcriptional regulator nuclear factor jB (NF-jB) (Tang et al., 2010; Yeung et al., 1999). Finally, peptidyl-prolyl cis–trans isomerase was increased in ovarian tissue (Ma et al., 2007) and is a member of the immunophilin protein family, which play a role in immunoregulation and basic cellular processes involving protein folding and trafficking. In summary, these studies provide confirmatory evidence for the reported association between PCOS, low-grade chronic inflammation and iron metabolism.

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The hemostatic system is frequently dysregulated in PCOS (Gonzalez et al., 2013; Karakurt et al., 2008; Lindholm et al., 2012; Manneras-Holm et al., 2011; Yildiz et al., 2002). Although the mechanisms underlying this association remain unclear, hyperglycemia, insulin resistance and compensatory hyperinsulinemia, several proinflammatory agents and dyslipidemia modulate the plasma levels of several hemostatic factors (Manneras-Holm et al., 2011). Hypofibrinolysis and thrombophilia may increase the spontaneous abortions rates and complications in pregnancy in patients with PCOS (Glueck et al., 1999, 2000), and impaired fibrinolysis might contribute to the risk of cardiovascular disease in this disorder (Wild et al., 2010). Several proteomic studies found differences in proteins involved in processes of vascular permeability, fibrinolysis, abnormal fibrogenesis and thrombosis. In whole ovarian tissue and granulosa cells antithrombin III, fibrinogen a chain, fibrinogen c chain, plasminogen-related protein A, and annexin 5 were increased in samples from patients with PCOS whereas annexin A2 was decreased (Choi et al., 2010; Ma et al., 2007). Annexin 5 was also increased in the visceral adipose tissue of this patients, whereas platelet basic proteins were increased in T lymphocytes (Corton et al., 2008; Misiti et al., 2010). The latter were also up-regulated in adipose tissue cDNA microarrays (Chazenbalk et al., 2012). Annexins, a class of Ca2+-regulated proteins, will be discussed in a following section (see biomolecules related to calcium metabolism below).

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Oxidative stress results from the imbalance between oxidant and antioxidant molecules derived from the generation of reactive oxygen species (ROS), from a decrease in antioxidant defense mechanisms, or both (Agarwal et al., 2012). Oxidative stress is present in many disorders related to insulin resistance including PCOS, in which hyperglycemia and increased free fatty acids induce the generation of ROS (Evans et al., 2003; Gonzalez et al., 2012; Murri et al., 2013). Proteins related to oxidative stress were increased in ovarian tissue, T lymphocytes and visceral adipose tissue from PCOS patients. The abundances of glutathione S-transferase M2 and NG,NG-dimethylarginine dimethylaminohydrolase 1 (DDAH1) were increased in ovarian tissue (Ma et al., 2007); superoxide dismutase (SOD) and peroxiredoxin-1 were found increased in T lymphocytes (Misiti et al., 2010); and glutathione S-transferase M3 (GSTM3) was increased in visceral adipose tissue (Corton et al., 2008). Only peroxiredoxin 2 was decreased in visceral adipose tissue (Corton et al., 2008). Two different transcript profiling strategies such as DNA microarrays and quantitative RT–PCR showed over-expression of the GSTM3 gene in PCOS omental adipose tissue

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(Corton et al., 2007). However, peroxiredoxin 2 gene expression was down-regulated in microarray analysis of ovarian theca cells (Wood et al., 2005). The glutathione S-transferases increased in PCOS samples belongs to the mu class of proteins. The mu enzymes are involved in the detoxification, by conjugation with glutathione, of electrophilic compounds that include carcinogens, drugs, environmental toxins and products of oxidative stress. Peroxiredoxins are important hydroperoxide detoxification enzymes and DDAH1 has a role in the regulation of nitric oxide generation by hydrolyzation of inhibitors of NOS. Together with the results of targeted studies (Baskol et al., 2011; Macut et al., 2011; Victor et al., 2009) these findings support the participation of oxidative stress in the pathogenesis of PCOS and its metabolic associations.

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10. Biomolecules related to calcium metabolism

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Calcium (Ca2+) is an essential signaling molecule involved in the regulation of many cellular functions, especially in the energy metabolism interplay of the cytosol with the mitochondria (Glancy and Balaban, 2012). Targeted studies suggested an association of disorders of Ca2+ and vitamin D metabolism with PCOS (Ott et al., 2012; Ranjzad et al., 2010; Thys-Jacobs et al., 1999). Several Ca2+-regulated proteins have evolved to maintain cellular Ca2+ at low levels (a prerequisite for signaling) and to couple changes in cytosolic Ca2+ to a wide spectrum of physiological responses (Gerke et al., 2005). Annexins are Ca2+-regulated proteins that bind to negatively charged phospholipids and establish specific interactions with other lipids and lipid microdomains. They participate in a many intracellular processes, from regulation of membrane dynamics to cell migration, proliferation, and apoptosis (Monastyrskaya et al., 2009). The abundance of annexins A6, A11 and A2 was decreased in ovarian tissue and granulosa cells from PCOS patients (Choi et al., 2010; Ma et al., 2007) whereas the abundance of annexin A5 in granulosa cells and visceral adipose tissue was increased (Corton et al., 2008; Choi et al., 2010). Calcium/calmodulin-dependent protein kinase II (CaMKII) was overabundant in ovarian tissue (Ma et al., 2007). CaMKII may link ER-stress with Fas and mitochondrial apoptosis pathways (Timmins et al., 2009). These results suggest that PCOS associates abnormalities in the homeostasis of calcium at the cellular level. A recent genome-wide association study showed that genes related to calcium signaling might be associated with PCOS (Shi et al., 2012). The gene location of calreticulin matches the 19q13.3 PCOS risk locus, and microarray analysis of cumulus cells demonstrated that the transcript levels of calreticulin are increased in PCOS samples (Jansen et al., 2004; Shi et al., 2012). The gene expression of osteoglycin was down-regulated in ovarian tissue microarrays (Jansen et al., 2004).

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11. Biomolecules with other functions

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The studies reviewed in this article have detected other proteins and metabolites, not included in previous sections, that may contribute to the pathophysiology of PCOS. Most of these proteins have been identified in ovarian tissue and include ribosomal or nuclear proteins among others (heterogeneous nuclear ribonucleoprotein A1, A2/B1 and A3, structural maintenance of chromosome 3, far upstream element binding protein 1 and 2, translation initiation factor eIF-2B) (Ma et al., 2007). The consequences of changes in the abundance of these proteins in the ovary of patients with PCOS remain unknown. However, the translation initiation factor eIF-2B, which was overabundant in PCOS ovarian tissue (Ma et al., 2007), is considered as a translational control of metabolic processes in response to stressors (Baird and Wek, 2012).

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Of note, the gene locations of three proteins (far upstream element binding protein 2, proliferation-associated protein 2G4 and stromal cell-derived growth factor SF20) overlap with the risk loci for PCOS identified by genome-wide association studies (Shi et al., 2012), and the genes encoding far upstream element binding protein 1 and ATP synthase-coupling factor 6, mitochondrial, were upregulated in microarray analyses of oocytes and theca cells from PCOS subjects (Kaur et al., 2012; Wood et al., 2005). Finally, some of the proteins identified in these studies are located in the mitochondria (ATP synthase-coupling factor 6, GTP:AMP phosphotransferase, voltage-dependent anion-selective channel protein 2), suggesting this organelle as the target for future proteomics and metabolomics approaches to PCOS.

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12. Summary and conclusions

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Nowadays PCOS is considered a complex multifactorial disorder resulting from the interaction of genetic, environmental, and lifestyle influences. In recent years, genomics, transcriptomics, proteomics and metabolomics have been applied as nontargeted tools with the aim of identifying molecules potentially involved in the pathophysiology of this frequent syndrome. The biomolecules identified so far participate in many metabolic pathways, including energy metabolism (glucose and lipid metabolism), protein metabolic processes and protein folding, cytoskeleton structure, immune response and inflammation, fibrinolysis and thrombosis, oxidative stress and intracellular calcium metabolism. These biomolecules provide key information about molecular functions altered in PCOS and raise new questions concerning their precise role in the pathogenesis of this disorder. Of note, the participation of some of these molecules in PCOS has been also confirmed by targeted studies. However, the proteomic and metabolomic studies reviewed here are not free of limitations. First no mechanistic conclusions can be derived because of their case-control design. Such a design does not clarify if the differences between PCOS and control samples in the abundance of biomolecules are causal to the syndrome, or are simply the consequence of the association of PCOS with other disorders. Second, the heterogeneity of the biological samples studied, the relatively small sample sizes, and the different proteomic and metabolomic techniques used in these studies imposes important limitations when integrating the results. Finally, the identification of proteins and metabolites showing very low abundance and the quantification of small-fold changes in abundance is close to the limit of resolution of currently available proteomics and metabolomics tools. Ongoing standardization efforts, such as the Human Proteome organization (HUPO) Proteomics Initiative (PSI), aim to facilitate data comparison, exchange, and verification. Together with the development of more effective tools for data analysis and interpretation, as well as continuing improvements in the sensitivity of mass spectrometry instrumentation, these standardization efforts might overcome some of the limitations of current strategies in the near future. In conclusion, nontargeted proteomic and metabolomic studies have identified novel proteins and metabolites, involved in different metabolic and cellular pathways, potentially involved in the pathogenesis of this syndrome. The biomolecules identified by these nontargeted approaches should be considered as candidates in future studies aiming to define specific molecular phenotypes of PCOS.

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Acknowledgments

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Supported by grants PI080944 and PI1100357 from Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spanish Ministry of Economy and Competitiveness, Spain.

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Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.mce.2013.02.009.

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Please cite this article in press as: Insenser, M., et al. Proteomic and metabolomic approaches to the study of polycystic ovary syndrome. Molecular and Cellular Endocrinology (2013), http://dx.doi.org/10.1016/j.mce.2013.02.009