Background: Polycystic ovary syndrome (PCOS) is a chronic condition with a reported prevalence of up to 18% and is associated with adverse long term outcomes. Structured education programmes have proved effective at optimising physical activity, biomedical outcomes and well-being of people with chronic conditions, however, pragmatic structured education interventions in women with PCOS are lacking. Aim: To develop an evidence-based structured education programme for women with PCOS and increase their step-count. Methods: Using a local multi-ethnic database, phenotypic presentation and long term cardiovascular outcomes of women with PCOS was determined. The attitudes of women with PCOS towards an education programme and their experience of living with PCOS was sought through qualitative interviews. A systematic review compared lifestyle interventions with insulin sensitizers in PCOS. Using the Medical Research Council’s framework, the SUCCESS education programme was developed and tested in a randomised controlled trial. Key Findings: • There are phenotypic differences in women with PCOS according to ethnicity or body weight. • Overweight and obese women with PCOS have a high proportion of cardiovascular risk factors and higher age-specific rates of myocardial infarction as compared to general female population. • In a meta-analysis, no statistical differences exist between the effect of lifestyle interventions and Metformin on BMI at six month. • Women with PCOS welcome group education programmes. They have significant body image issues, which has an emotional impact. • The SUCCESS education programme did not increase the step count at three month. Conclusion: This project established the high cardiovascular risk associated with PCOS. Although, the SUCCESS education programme did not show positive results at three months, it is the first pragmatic structured education programme tailored to women with PCOS. Outcomes at the final analysis in 12 months, will inform whether the programme should be implemented or adapted further and re-evaluated.

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The aim of this thesis was to generate "artificial" antibodies against a bioactive protein, GnSAF, produced by human ovarian granulosa cells that remains unidentified after 20 years of research. The library used in this study was a synthetic single-chain antibody scFv, Tomlinson J library. The antigen for biopanning was partially purified GnSAF. Screening the antibody clones from the library incorporated an additional selection step: an in vitro rat monolayer bioassay for GnSAF based on the specific suppression of GnRH-induced LH secretion. The initial screening with a binding ELISA technique resulted in 8 clones that were tested by bioassay, initially as pooled phage forms and subsequently as individual soluble scab forms. Three scabs recognised GnSAF bioactivity; with the suppression of GnRH-induced LH secretion by GnSAF-containing preparations reduced by up to 50% following incubation with the scabs. In order to improve the stability of the scabs for immunopurification purposes, and to widen the range of secondary labelled-antibodies available, the scabs were engineered into full length human immunoglobulins (IgG). One clone of the purified IgG form significantly altered GnSAF dose-response curves and demonstrated high affinity for GnSAF bioactivity when immobilised. When used for repeated immunopurification cycles and then Western blotted, this antibody enabled the isolation of a distinct band at around 66 kDa suggesting that this might be GnSAF. The main candidate molecules identified from the immunopurified material by excision of 2-D gel protein spots was a human serum albumin precursor and alloalbumin. This study demonstrates that the combination of bioassay and phage display technologies is a powerful tool in the study of uncharacterised proteins.

The ovary is the female reproductive organ where the egg cells are stored, and where hormones are produced. A follicle is a cell aggregation in the ovary which consists of an egg (oocyte) and supporting cells. Throughout the life of each woman, follicles initiate growth, moving from the primordial stage to the primary stage. Subsequently they move to the secondary stage, and then to the tertiary stage. Consequently an understanding of the mechanisms of initiation and development of follicles is of fundamental importance. It may also be helpful to the many women in which the process does not proceed properly; this may be useful for the treatment of infertility in females, and also the in vitro maturation of follicles. The main contribution of this thesis is in the discovery of novel scaling relationships in the human and in the mouse, between primordial follicles and growing follicles. These scaling relationships suggest that the number of follicles entering the growing pool is not constant over time, and yet they also suggest that the proportion of growing follicles to primordial follicles increases as age Increases. ODE models are used in this work; they are models comprising Ordinary Differential Equations, which are equations containing functions of one independent variable (in this case, time) and derivatives of these functions. The scaling relationships are used to simplify ODE models of the dynamics of initiation and atresia (degeneration). A relationship between the rate of initiation and the rate of atresia is predicted. Furthermore. interesting biological questions can be asked. If initiation is controlled by signals (hormones, growth factors, etc), are they the same throughout life, or is there evidence for different mechanisms in the mature adult, and prior to the menopause?

There is a frequent concurrence of obesity and polycystic ovary syndrome (PCOS). To confirm the mechanistic link between these two common conditions, the studies detailed in this thesis have employed both genetic and physiological approaches.

Polycystic ovary syndrome (PCOS) is characterised by hyperandrogenism and insulin resistance. Granulosa cells in PCOS demonstrate impaired insulin-induced glucose uptake and lactate accumulation, suggesting a post receptor, signalling pathway-specific impairment of insulin action. Gonadotrophins are also important in the regulation of glucose metabolism by of granulosa cells. The first objective of this project was to use KK1 cells an immortalised mouse granulosa cell line, to characterise insulin, androgen and FSH signalling as well as glucose metabolism. Cell lysates were subjected to western immunoblotting for key proteins in the insulin signalling pathways. Glucose metabolism of KK1 cells was also measured. Surprisingly, androgen alone stimulated glucose uptake and lactate production and augmented insulin-induced glucose metabolism. This suggests that the insulin resistance observed in granulosa cells from women with PCOS is not a direct effect of exposure to androgen. The main part of the thesis was examination of insulin action on human primary ovarian granulosa-lutein (GL) cells to investigate the mechanism of insulin resistance in PCOS. Insulin and FSH signalling in GL cells from women with anovulatory PCOS (anovPCO, n=11) was compared to that in GL cells from ovulatory women with (ovPCO, n=8) and without polycystic ovaries (controls n=12). Primary GL cells were incubated with insulin or FSH and analysed for glucose metabolism and progesterone production. Cell lysates were prepared for identification of signalling pathways, using western immunoblotting. The results confirmed selective impairment of glucose metabolism in cells from anovulatory PCOS. No significant impairment of insulin stimulated PI3K signalling was observed. However there was a reduction of p42/44ERK phosphorylation in the ovulatory PCOS group compared to controls. The significance of this finding with respect to impaired glucose metabolism in granulosa cells remains to be determined. We also showed FSH induced glucose metabolism, but without clear evidence of activation of the PI3-kinase pathway.

The theme of the work presented in this multi-disciplinary PhD is the development of new computational tools and techniques to study and understand spatio-temporal follicle growth in neonatal mouse ovaries. The female ovary is endowed at birth with a finite, non-renewable supply of oocytes, each enclosed in a layer of supporting somatic (granulosa) cells to form a quiescent follicle. From birth, a steady trickle of follicles initiate growth to maintain a supply of mature oocytes for regular ovulation. Disruption in the regulation of initiation of follicle growth can result in various pathologies, such as premature ovarian failure and polycystic ovary syndrome. The mechanism of regulation of the initiation of follicle growth remains unclear, but may involve inter-follicle signaling via paracrine growth factors. To investigate this hypothesis, a new technique for quantifying and analyzing spatial distributions of quiescent and growing follicles in the adult human has been developed, as an extension of a novel technique previously developed in neonatal mice in our laboratory. As in the mouse study, we have found evidence that in the human ovary neighbouring quiescent follicles inhibit follicle growth, at a small range. This approach has been further extended to cultured neonatal mouse ovaries, which in vitro lack a systemic blood supply, to investigate the relative contributions of inter-follicle paracrine signaling and endocrine growth factor/nutrient signaling to the regulation of initiation of follicle growth. Accurate counts of the numbers of follicles in ovaries are important for a wide variety of studies of ovarian physiology, including investigating the effects of age, toxins, chemotherapeutics, endocrine disruptors and specific genes (knock out/transgenic studies) on follicle formation, endowment and development. Many published studies use frequent sampling of a small number of ovaries (often as few as three) to obtain estimates of the number of follicles. We have tested the validity of this approach by generating 3D spherical simulated ovaries which contain realistic numbers of follicles at different stages and which are realistically positioned within these ovaries. The number and position of follicles is based on real biological data. This model enables us to rapidly ‘virtually’ section the ovary in silico and obtain computer-generated counts of the numbers of follicles in sections at different frequencies, such as one every fifth section (1/5), 1/20 or 1/50. As we know precisely how many follicles each simulated ovary contains, we can compare the accuracy using different sampling frequencies of varying numbers of ovaries. This has enabled us to demonstrate that the error is smaller when infrequent sampling of a large number of ovaries (≥8) is carried out, and that this actually involves analyzing fewer sections overall. We have gone on to generate simulated ovaries from knockout mice, with more or fewer follicles, and can predict how many ovaries are required to make robust comparisons between knockout and control animals. This has shown that biological variability contributes more to counting error than the method of sampling. These simulated ovaries provide a unique resource to model large studies. Currently follicle counts are obtained by fixing and serially sectioning ovaries, and manually counting the follicles in sections. This is laborious and time-consuming. Faster methods of obtaining follicle estimates are required. With the use of confocal microscopy and immunohistochemistry for an oocyte-specific protein, we were able to establish a protocol that allows us to image and computationally reconstruct a whole neonatal mouse ovary in 3D. Follicle number can be estimated rapidly using a stereologic method. The stereologic technique error was estimated using the simulated ovary model, leading to the conclusion that the method can be safely used to obtain rapid estimates of follicle number. The time required can be further reduced by using image processing to detect the stained follicles on the sections. We have developed an algorithmic technique that can instantaneously identify stained oocytes, count them, and calculate their spatial distribution. A fundamental unanswered question is whether follicles move in the ovary, particularly as they grow. This question has arisen from the observation that small follicles tend to be situated close to the ovarian surface, while large ones are closer to the medulla. This question has implications for interfollicle signaling. We have developed a protocol to image the ovary while in culture using timelapse confocal and live lipid stains to visualize the follicles. Results show that small follicles are not moving significantly over a period of 12h. This project can be extended in the future with the use of transgenic mice for GFP tagging, to accurately monitor changes in structures of interest within cultured ovaries.

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