title: Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia.
abstract: Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite) and protein-protein associations (STRING). This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome). The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300) and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.
authors.affiliation: Reproductive Biology Laboratory, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Miranda
authors.initials: M
authors.lastname: van Uitert
authors.affiliation: Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam, Amsterdam, the Netherlands.
authors.forename: Perry D
authors.initials: PD
authors.lastname: Moerland
authors.affiliation: Department of Epidemiology, University of Washington, Seattle, WA, United States of America.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Enquobahrie
authors.affiliation: Medical Genetics and Obstetrics and Gynaecology, and the Institute for Molecular Medicine Finland, University of Helsinki and Helsinki University Hospital, Finland.
authors.forename: Hannele
authors.initials: H
authors.lastname: Laivuori
authors.affiliation: Women's and Children's Clinic, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Joris A M
authors.initials: JA
authors.lastname: van der Post
authors.affiliation: Reproductive Biology Laboratory, Academic Medical Center, Amsterdam, the Netherlands; Women's and Children's Clinic, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Carrie
authors.initials: C
authors.lastname: Ris-Stalpers
authors.affiliation: Reproductive Biology Laboratory, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Gijs B
authors.initials: GB
authors.lastname: Afink
mesh_terms: D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D010920:Placenta / Q000378:metabolism*; D011225:Pre-Eclampsia / Q000235:genetics* / Q000378:metabolism; D011247:Pregnancy; D025941:Protein Interaction Mapping