Galectins are highly expressed at the maternal-fetal interface, and regarded as multifunctional regulators of fundamental cellular processes due to their capacity to modulate functions such as cell-extracellular matrix interactions, proliferation, adhesion, and invasion. Galectin 3 is expressed in placental cell columns, but not in invasive extravillous trophoblasts. The negative correlation between expression and trophoblast invasiveness is in accordance with our finding of decreased galectin 3 expression over gestation. Galectin 8 is expressed by decidual cells, villous and extravillous trophoblasts, but its role is less clearly understood. The IPA analyses of the 22 PE-associated transcripts demonstrated an over-representation of genes associated with metabolic disease. PE share several metabolic abnormalities with cardiovascular diseases and diabetes, and having a PE complicated pregnancy is associated with increased risk of type 2 diabetes later in life. This agrees with pregnancy acting as a stress factor which could reveal a pre-existing 
disposition to later life metabolic disease. IPA analysis of the 92 transcripts that were associated with gestational age revealed an over-representation of genes involved in cell assembly and organisation, tissue development, cellular movement, tissue morphology, and connective tissue development and function. These findings are in agreement with known biological processes taking place at the maternal-fetal interface during pregnancy, such as trophoblast proliferation, differentiation, invasion and extracellular matrix remodelling. It is important to consider that the data used in our study was produced on two different microarray platforms, and that tissue sampling procedures differed between the three sub-studies included in our dataset. In dataset #1 and #2, basal plate biopsies were used for transcriptional analyses, whereas in dataset #3, decidual tissue was collected by vacuum suction of the entire placental bed. These differences may pose a potential bias, as gene expression has been shown to differ depending on tissue sampling site. Inter- and intra-platform reproducibility has been shown to be good in terms of detecting differentially expressed genes. However, the reproducibility of absolute Acetylcorynoline transcription levels is poor, and pre-processing is required before comparisons of transcription values across platforms can be made. To deal with these limitations, and be able to make both inter- and intraplatform comparisons of transcription values, we performed inverse normal transformation. After this transformation, the distribution of transcription values is assumed equal for all probes, independent of study, sampling method and platform. However, PE-status, gestational age and tissue sampling method differed between studies, and consequently we had to include this as a factor Ginsenoside-Ro giving a separate level for each study in the linear regression model. In our analyses, we exclusively searched for observations that agreed between datasets, and the limitations mentioned above will rather reduce the power of identifying differentially expressed genes than introduce false positive results. Combined with the fact that our pooled dataset only targets 14,678 genes, and that we are using a very stringent threshold for significance, our results likely represent only a small fraction of the total number of transcripts that are influenced by either PE or gestational age. Had all samples been collected by the same method and analysed on the same arrays, it is likely that a much larger number of differentially expressed transcripts would be identified.