Plasma plasminogen activator inhibitor-1 levels in the different phenotypes of the polycystic ovary syndrome

Koiou, Ekaterini; Τζιόμαλος, Κωνσταντίνος; Dinas, Konstantinos; Katsikis, Ilias; Kandaraki, Eleni; Tsourdi, Elena; Mavridis, Stylianos; Panidis, Dimitrios

doi:10.1507/endocrj.ej11-0023

Cited by 16 publications

(14 citation statements)

References 38 publications

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“…Although serpins, in particular serpin E1, have been associated with e.g. polycystic ovary syndrome (18,19), serpin B3 and 4 have so far only been described in cystic teratomas of the ovary (20). Further studies beyond the scope of this work will be necessary to understand the biological and biomedical implications of this finding.…”

Section: Resultsmentioning

confidence: 99%

MStern Blotting–High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates

Berger

Ahmed

Muntel

et al. 2015

Molecular & Cellular Proteomics

107

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We describe a 96-well plate compatible membrane-based proteomic sample processing method, which enables the complete processing of 96 samples (or multiples thereof) within a single workday. This method uses a large-pore hydrophobic PVDF membrane that efficiently adsorbs proteins, resulting in fast liquid transfer through the membrane and significantly reduced sample processing times. Low liquid transfer speeds have prevented the useful 96-well plate implementation of FASP as a widely used membrane-based proteomic sample processing method. We validated our approach on whole-cell lysate and urine and cerebrospinal fluid as clinically relevant body fluids. Without compromising peptide and protein identification, our method uses a vacuum manifold and circumvents the need for digest desalting, making our processing method compatible with standard liquid handling robots. Mass spectrometry (MS)-based proteomics is moving increasingly into the translational and clinical research arena, where robust and efficient sample processing is of particular importance. The conventional sample processing methods in proteomics, namely SDS-PAGE, or in-solution-based sample processing, are slow and laborious and thus do not easily provide the reproducibility and throughput to meet current demands. A paradigm shift was the introduction of a filteraided sample processing method (FASP), which is initially described by Manza et al.(1) and then fully realized in practice by Wisniewski et al. (2). These filter-aided methods make use of ultrafiltration membranes with molecular weight cut offs (MWCO) in the 10 to 30 kDa range to efficiently remove small molecules and salts and to capture denatured proteins on a cellulose filter even if the molecular weight of the protein is much smaller than the nominal MWCO of the ultrafiltration membrane. Thus, the denaturation step is crucial to ensure that proteins much smaller than the nominal MWCO are efficiently retained by, e.g. a 10 kDa MWCO filter.In translational and clinical proteomics, which normally include large cohorts, the multititer-well plate is the preferred format for sample processing and storage. Although the application of FASP in the 96-well plate format has been described (3, 4), the major limitation of FASP in the 96-well plate is the much slower speed at which the 96-well plates have to be centrifuged: while a single ultrafiltration unit withstands up to 14,000 ϫ g, the 96-well plate format can only be centrifuged at g-forces of up to 2,200 ϫ g. This significantly lower g-force for 96-well plates results in a slow liquid transfer, which in turn considerably prolongs the required centrifugation times to hours instead of tens of minutes for the three to four necessary centrifugation steps (i) for the initial loading, reduction and alkylation, (ii) for the different washing steps, and (iii) for the elution (3).Independent of the format FASP is performed in, the conventional FASP also requires relative large volumes of high salt concentration for efficient elution of the tryptic peptid...

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Section: Resultsmentioning

confidence: 99%

MStern Blotting–High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates

Berger

Ahmed

Muntel

et al. 2015

Molecular & Cellular Proteomics

107

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“…As hyperinsulinemia can increase androgen secretion, a PCOS characteristic, it is believed that hyperinsulinism might contribute to PCOS development [32, 33], and can also lead to the development of Glu intolerance and type 2 diabetes [3, 4]. necessitating more attention be given to ethnic differences in insulin receptor action in PCOS [7].…”

Section: Discussionmentioning

confidence: 99%

“…Polycystic ovary syndrome (PCOS) is one of the most common, complex and heterogeneous endocrine disorders [1–4] affecting 6-10% of women of fertile age [5, 6]. Although the etiology of PCOS is still unclear, genetic and environmental factors have been considered as possible contributors [1, 3, 7].…”

Section: Introductionmentioning

confidence: 99%

The prevalence of metabolic disorders in various phenotypes of polycystic ovary syndrome: a community based study in Southwest of Iran

Tehrani

Rashidi

Khomami

et al. 2014

Reprod Biol Endocrinol

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BackgroundPolycystic ovary syndrome (PCOS) is a common endocrinopathy, associated with metabolic abnormalities. Metabolic features of various phenotypes of this syndrome are still debatable. The aim of present study hence was to evaluate the metabolic and hormonal features of PCOS phenotypes in comparison to a group of healthy control.MethodsA total of 646 reproductive-aged women were randomly selected using the stratified, multistage probability cluster sampling method. The subjects were divided into five phenotypes: A (oligo/anovulation + hyperandrogenism + polycystic ovaries), B (oligo/anovulation + hyperandrogenism), C (hyperandrogenism + polycystic ovaries) and D (oligo/anovulation + polycystic ovaries). Hormonal and metabolic profiles and the prevalence of metabolic syndrome among these groups were compared using ANCOVA adjusted for age and body mass index.ResultsAmong women with PCOS (n = 85), those of groups A and C had higher serum levels of insulin and homeostatic model assessment for insulin%20resistance (HOMA-IR), compared to PCOS women of group D. Serum concentrations of cholesterol, low density lipoprotein, triglycerides and glucose in group A were higher than in other phenotypes, whereas the metabolic syndrome was more prevalent among group B.ConclusionsWomen who had all three components of the syndrome showed the highest level of metabolic disturbances indicating that metabolic screening of the severest phenotype of PCOS may be necessary.Electronic supplementary materialThe online version of this article (doi:10.1186/1477-7827-12-89) contains supplementary material, which is available to authorized users.

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“…At the time of participant recruitment, it was difficult to calculate the exact sample size needed for this study as limited previous literature has assessed the end‐point measures of the other biomarkers in lean and overweight women with and without PCOS. Previous work has investigated lean and overweight controls in comparison to lean and overweight PCOS women divided based on phenotype for PAI‐1. Based on these results using overweight and lean controls and lean Rotterdam and overweight NIH groups, a total sample size of n = 56 was required to observe differences in PAI‐1 at 80% power ( P < 0·05).…”

Section: Methodsmentioning

confidence: 99%

Biomarkers and insulin sensitivity in women with Polycystic Ovary Syndrome: Characteristics and predictive capacity

Cassar

Teede

Harrison

et al. 2014

Clinical Endocrinology

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Plasma plasminogen activator inhibitor-1 levels in the different phenotypes of the polycystic ovary syndrome

Cited by 16 publications

References 38 publications

MStern Blotting–High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates

MStern Blotting–High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates

The prevalence of metabolic disorders in various phenotypes of polycystic ovary syndrome: a community based study in Southwest of Iran

Biomarkers and insulin sensitivity in women with Polycystic Ovary Syndrome: Characteristics and predictive capacity

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