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Transcription Analysis of the Myometrium of Labouring and Non-Labouring Women

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  • Gemma C Sharp
  • James L Hutchinson
  • Nanette Hibbert
  • Tom C Freeman
  • Philippa T K Saunders
  • Jane E Norman

Abstract

An incomplete understanding of the molecular mechanisms that initiate normal human labour at term seriously hampers the development of effective ways to predict, prevent and treat disorders such as preterm labour. Appropriate analysis of large microarray experiments that compare gene expression in non-labouring and labouring gestational tissues is necessary to help bridge these gaps in our knowledge. In this work, gene expression in 48 (22 labouring, 26 non-labouring) lower-segment myometrial samples collected at Caesarean section were analysed using Illumina HT-12 v4.0 BeadChips. Normalised data were compared between labouring and non-labouring groups using traditional statistical methods and a novel network graph approach. We sought technical validation with quantitative real-time PCR, and biological replication through inverse variance-weighted meta-analysis with published microarray data. We have extended the list of genes suggested to be associated with labour: Compared to non-labouring samples, labouring samples showed apparent higher expression at 960 probes (949 genes) and apparent lower expression at 801 probes (789 genes) (absolute fold change ≥1.2, rank product percentage of false positive value (RP-PFP)

Suggested Citation

  • Gemma C Sharp & James L Hutchinson & Nanette Hibbert & Tom C Freeman & Philippa T K Saunders & Jane E Norman, 2016. "Transcription Analysis of the Myometrium of Labouring and Non-Labouring Women," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-21, May.
    • Handle: RePEc:plo:pone00:0155413
    DOI: 10.1371/journal.pone.0155413
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    References listed on IDEAS

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    1. Tom C Freeman & Leon Goldovsky & Markus Brosch & Stijn van Dongen & Pierre Mazière & Russell J Grocock & Shiri Freilich & Janet Thornton & Anton J Enright, 2007. "Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-11, October.
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