Network analysis of genomic regulators of labour using in silico techniques

    Student thesis: Phd


    AbstractPreterm birth is a worldwide health issue, with increased incidence of fetal mortality and risk of morbidities. Inhibition of myometrial contractions is the only therapeutic intervention available, but this strategy remains limited in effectiveness as labour is initiated by a network of processes. The regulation of these mechanisms, and the coordination between different uterine tissues, is poorly understood. In order to develop effective therapies, we must first define and understand the processes occurring prior to and during labour. We wished to explore the interactions that occur in the decidua and myometrium, identify the master regulators within the global system of labour and select potential therapeutic candidates for suppression of multiple labouring processes. The detailed investigation of the role of the decidua in labour is critically important, as it is the site of significant inflammatory, progesterone and prostaglandin activity during labour. Using a global approach of detection and analysis, this study used high through-put microarrays, pathway and network analysis, an in silico prediction workflow, and in vitro inhibition of candidate proteins in human and mouse cell lines to identify molecular fingerprint of labour, characterise the changes occurring near the end of pregnancy that lead towards activation of labour, and identify master regulators of these pathways. In human choriodecidua, the most enriched pathways of global gene expression during term labour were inflammatory-associated. Network analysis identified vimentin, TLR4 and TNFSF13B as master regulators of labour in the decidua and identified MT2, TLR2 and RelB in the myometrium. Preliminary in vitro inhibition experiments indicated blockade of individual master regulators could moderately decrease expression of multiple downstream labour-associated genes. The mouse decidua was also characterised by the presence of a significant inflammatory response during active term labour. However, major changes in decidual function and regulators were evident prior to labour; these processes were initiated as early as 60 hours preceding labour. The mouse myometrium was enriched for chemokine pathways, but the majority of gene changes occurred only 12 hours prior to labour in this tissue. In the mouse, in silico predicted master regulators included IRAK4, TIRAP in the decidua and the TLR2/4 complex in the myometrium. In conclusion, this study has shown for the first time that in normal term labour, the human choriodecidua is highly functional with active sterile inflammatory processes occurring. The findings of widespread preparatory events in the murine decidua, that precede changes in the myometrium, suggests integral roles in the initiation of labour. There are highly conserved similarities in the inflammatory receptors, ligands and transcription factors between human and mouse tissues. The master regulators identified govern multiple downstream genes are that not directly associated with contraction, indicating there are multiple important processes occurring during labour as well as the contractile factors. Although some of the regulators have been previously identified as being associated with labour, this study was the first to identify their network regulatory characteristics. Instead of targeting a single and potentially late-stage event such as myometrial contraction, targeting multiple processes with the inclusion of processes in the closely-related and highly functional decidua, may be a more effective strategy in preventing progression of the labour process.
    Date of Award1 Aug 2016
    Original languageEnglish
    Awarding Institution
    • The University of Manchester
    SupervisorAdam Stevens (Supervisor), Lynda Harris (Supervisor), Clare Tower (Supervisor) & Rebecca Jones (Supervisor)


    • Network analysis
    • Systems biology
    • Microarray
    • Pathway analysis
    • Labour/ labour
    • Myometrium
    • Decidua
    • Genes
    • Transcriptome

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