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

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) <0.05). Although half of the women in the labouring group had received pharmaceutical treatment to induce or augment labour, sensitivity analysis suggested that this did not confound our results. In agreement with previous studies, functional analysis suggested that labour was characterised by an increase in the expression of inflammatory genes and network analysis suggested a strong neutrophil signature. Our analysis also suggested that labour is characterised by a decrease in the expression of muscle-specific processes, which has not been explicitly discussed previously. We validated these findings through the first formal meta-analysis of raw data from previous experiments and we hypothesise that this represents a change in the composition of myometrial tissue at labour. Further work will be necessary to reveal whether these results are solely due to leukocyte infiltration into the myometrium as a mechanism initiating labour, or in addition whether they also represent gene changes in the myocytes themselves. We have made all our data available at www.ebi.ac.uk/arrayexpress/ (accession number E-MTAB-3136) to facilitate progression of this work.

Fig 1. A heatmap to show how genes and samples cluster based on similar expression levels.

The bar at the top indicates the sample group (pink = labouring, blue = non-labouring). Normalised standardised expression values are indicated on a colour scale with purple indicating high expression and cyan indicating low expression. The heatmap was created using genes with a non-labour to labour fold change of >2 or <-2 and an RP-PFP ≤0.0001.

Fig 2. Sample-sample network graph, in which each node represents a different sample.

Edges are coloured to reflect the Pearson correlation that they represent. Red edges represent a high correlation and blue edges represent a low correlation. The same graph is coloured by A. unbiased MCL cluster number, B. labour status, C. parity, D. gestational age at delivery, E. body mass index (BMI), and F. pharmaceutical treatment.

Fig 3. Probe-probe network graph, in which each node represents a different probe.

Nodes are coloured according to membership of different MCL clusters. The graphs show the mean expression profiles of clusters 1,2,3,4 and 15. Samples are plotted on the x-axes: non-labouring samples are represented by the pink bar and labouring samples are represented by the blue bar. Error bars indicate standard errors.

Fig 4. Forest plots to illustrate the standardised mean difference between labouring and non-labouring groups in Sharp, Weiner and Bukowski.

Summary statistics, indicated by the blue diamond, were calculated via inverse variance weighted meta-analysis. The array platform used by Bukowski did not cover all selected genes, so Bukowski could not be included in all meta-analyses.