Differentially expressed microRNAs and affected biological pathways revealed by modulated modularity clustering (MMC) analysis of human preeclamptic and IUGR placentas

Introduction This study focuses on the implementation of modulated modularity clustering (MMC) a new cluster algorithm for the identification of molecular signatures of preeclampsia and intrauterine growth restriction (IUGR), and the identification of affected microRNAs Methods Eighty-six human placentas from normal (40), growth-restricted (27), and preeclamptic (19) term pregnancies were profiled using Illumina Human-6 Beadarrays. MMC was utilized to generate modules based on similarities in placental transcriptome. Gene Set Enrichment Analysis (GSEA) was used to predict affected microRNAs. Expression levels of these candidate microRNAs were investigated in seventy-one human term placentas as follows: control (29); IUGR (26); and preeclampsia (16). Results MMC identified two modules, one representing IUGR placentas and one representing preeclamptic placentas. 326 differentially expressed genes in the module representing IUGR and 889 differentially expressed genes in a module representing preeclampsia were identified. Functional analysis of molecular signatures associated with IUGR identified P13K/AKT, mTOR, p70S6K, apoptosis and IGF-1 signaling as being affected. Analysis of variance of GSEA-predicted microRNAs indicated that miR-194 was significantly down-regulated both in preeclampsia (p = 0.0001) and IUGR (p = 0.0304), and miR-149 was significantly down-regulated in preeclampsia (p = 0.0168). Discussion Implementation of MMC, allowed identification of genes disregulated in IUGR and preeclampsia. The reliability of MMC was validated by comparing to previous linear modeling analysis of preeclamptic placentas. Conclusion MMC allowed the elucidation of a molecular signature associated with preeclampsia and a subset of IUGR samples. This allowed the identification of genes, pathways, and microRNAs affected in these diseases.