The diagnostic and prognostic role of proteomics and metabolomics in Polycystic Ovary Syndrome

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder, affecting up to 10% -26% of women of reproductive age. Diagnostic criteria is somewhat controversial but ever evolving. PCOS is characterized by an adverse metabolic profile (dyslipidaemia, diabetes, obesity), increased susceptibility to develop pregnancy complications like miscarriage, preterm labour, pre-eclampsia (PE) and cardiovascular disease (CVD) later in life. Predicting which women with PCOS will go on to develop PE and other major long-term health implications, however remains a challenge in routine clinical practice. We embarked on a journey to explore whether proteomic, and metabolomics studies would unravel the pathophysiology and help us understand the diagnostic criteria and prognostic features of Polysystic ovarian syndrome. The studies in this thesis aimed to address some of these challenges through the identification of a proteomic and metabolomic biomarker fingerprint unique to PCOS that could aid diagnosis.This might help improve our understanding of it’s aetiology and if associated with PE, form the basis of future validation studies to aid the prediction of which women with PCOS were more likely to develop PE. The null hypothesis was that women with PCOS did not have a proteomic and metabolomic and genomic profile unique to PCOS and PE. That, computational analysis would not reveal a single unifying pathway which could improve the diagnosis of PCOS and predict which women with PCOS would develop long term health complications and PE. First, a systematic review was performed to identify proteomic biomarkers similarly expressed in women with PE and PCOS.This was to help the subsequent synthesis of these results with primary metabolomics data and obtain a comprehensive overview of novel pathways potentially linking PCOS with PE. One hundred and ninety two proteomic biomarkers for PE were cross-referenced with a publicly available PCOS proteomic biomarker database to determine if any were also differentially expressed in PCOS. Five biomarkers were found to be differentially expressed in women with PE and with PCOS compared to controls. These included transferrin, fibrinogen alpha, beta and gamma chain variants and kininogen-1 which were raised and annexin 2 and peroxiredoxin 2 were decreased in both women with PCOS and with PE. In PE these biomarkers were found in serum, plasma and placenta respectively whereas in PCOS the biomarkers identified were in serum, follicular fluid, ovarian and omental biopsy respectively. Next, a cross sectional study involving 59 women with PCOS and 61 controls was undertaken to identify oxylipin biomarkers (metabolomics biomarkers markers of cardiovascular disease and PE) unique to PCOS. Fasting blood samples were collected and serum extracted after centrifugation for 10 minutes. Samples were stored at -80 degrees centigrade for later analysis. Solid Phase Extraction, Liquid Chromatograph Mass Spectrometry was used for analyzing the samples, which were tested for 20 oxylipins in a targeted manner. Weight, BMI and waist: hip ratio were all higher in PCOS cohort compared to the controls. The results found that of the 19 Oxylipins analysed in the plasma samples, 14,15 dihydroxyeicosatrienoic acid (DHET) AND 19 Hydroxyeicosatetraenoic acid (HETE) were found to be significantly raised in PCOS women compared to the controls (p=<0.05). When adjustments were made for BMI and waist: hip ratio, 14,15 DHET was found to be still significant but on the other hand, 19 HETE was only significantly high in PCOS group when adjusted for waist: hip ratio only. Finally, a computer based network analysis of lipids was performed. A software was used to see whether there were any common pathways shared by PE and PCOS identified from the published metabolomic studies. A literature search was performed on metabolomics (lipid) biomarkers in women with PE compared with controls. I also carried out the same exercise in the only published study on metabolomic lipid biomarkers expressed differently in women with PCOS compared with controls. Data was then inputted into a novel lipidomic (metabolomic lipid biomarkers) network containing all lipid reactions that are found in public databases to identify smaller regional networks and key genes that potentially played a pivotal role in the mechanisms of PCOS and PE. The network analysis on lipids in PCOS found 3 key genes (CHPT1, PTDSS1 and PLD1).The network analysis of lipids in PE,revealed five genes (CPT1, CPT2, ACOT2, PTDSS1 and CHPT1). Two genes (PTDSS1, CHPT1) were comparable in both the PE and PCOS lipids.However, PTDSS1 was the only gene similarly over expressed in both PCOS and PE. I was not able to identify a single unifying pathway from the list of biomarkers and genes identified in my study. My null hypothesis was therefore not rejected. The proteomic biomarkers common to PCOS and PE are involved in a range of diverse molecular pathways including immunoregulation/inflammation, fibrinolysis,iron transport and blood coagulation.The oxylipins differentially expressed in PCOS are involved in cardio-protection, vasoconstriction, renal function, vascular tone and arterial pressure, inflammation, oxidative stress, diabetes, impaired insulin signalling, platelet aggregation and generation of thrombin. Finally, the two genes (PTDSS1, CHPT1) identified in both the PE and PCOS lipid network analyses are associated with Lenz-Majewski hyperostotic dwarfism, glycerophospholipid and cholesterol metabolism, kidney ischemia-reperfusion injury and the formation and maintenance of vesicular membranes. As far as I know, this is the first study that has investigated the links between PCOS and PE using this unique combination of “omic” approaches complemented by computational analysis of a novel lipid network to identify novel genes and pathways linking PCOS and PE approach. More mechanistic studies are however required to investigate the inter-relationships between the proteomic biomarkers, oxylipins and genes identified from my lipidomic network analysis to improve our understanding of the mechanisms linking PCOS and PE. The diverse mechanistic pathways identified in my study may however reflect the complexity of both PCOS and PE, and it may well be that a range of factors (molecular, lifestyle, environmental and intra-uterine factors) all interact to result in both phenotypes. Future research would ideally require large collaborations so that the methodology and interpretation of data is consistent.