Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.632950
Title: Modelling physiological reproductive inflammatory networks in vivo
Author: Field, Sarah Louise
ISNI:       0000 0004 5364 458X
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2014
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
The immune and reproductive systems have long been known to be inextricably linked, with components of immune pathways, particularly cytokines, mediating processes such as ovarian/menstrual cyclicity, endometrial remodelling, mating-induced immunomodulation, implantation, pregnancy, parturition and lactation. The nature of this involvement has often been investigated at the level of single mediators, with little consideration of the fact that cytokines are increasingly understood to function as complex networks. This study aimed to characterise inflammatory networks using both traditional and novel machine-learning Bayesian network-based methods in the context of keystone aspects of reproduction, viz., in the endometrial response to seminal plasma, cytokine:hormone interactions during lactation, and oocyte maturation following controlled ovarian hyperstimulation. ‘Traditional’ pathway analyses used to examine the murine endometrial response to seminal plasma revealed previously unidentified mediators and showed compartmentalised epithelium/stroma-specific responses. However, they proved ineffective in describing novel cytokine interactions. This led to the development a highly effective novel Bayesian network-based approach to explore cytokine:hormone networks during murine lactation. This revealed that prolactin, a putative potent immunomodulator, was far less influential than expected in vivo. The method also identified previously unknown cytokine interactions and described features such as synergy and antagonism. Further refinement of these network analyses as modified variational Bayesian state space models enabled the display of core, conserved subnetworks (communities) of human follicular fluid cytokines whose interactions varied with oocyte maturity. Moreover, these cytokine signatures also allowed the prediction of an oocytes’ fertilisability potential, with potential attendant benefits to assisted conception. This thesis represents the first endeavour to model inflammatory networks in vivo in any setting to date. It has revealed their central role, functional conservation and key features of cytokine interactions across a spectrum of reproductive processes. Further development of this methodology appears set to offer invaluable new insights into the complex immune signalling that underpins reproductive biology.
Supervisor: Orsi, Nicolas M. ; Simpson, Nigel A. B. ; Burns, Philip A. Sponsor: Infertility Research Trust ; Wellbeing of Women
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.632950  DOI: Not available
Share: