Alternative splicing in normal and pathological human placentas is correlated to genetic variants.
Camino S M RuanoClara ApicellaSébastien JacquesGéraldine GascoinCassandra GasparFrancisco MirallesCéline MéhatsDaniel VaimanPublished in: Human genetics (2021)
Two major obstetric diseases, preeclampsia (PE), a pregnancy-induced endothelial dysfunction leading to hypertension and proteinuria, and intra-uterine growth-restriction (IUGR), a failure of the fetus to acquire its normal growth, are generally triggered by placental dysfunction. Many studies have evaluated gene expression deregulations in these diseases, but none has tackled systematically the role of alternative splicing. In the present study, we show that alternative splicing is an essential feature of placental diseases, affecting 1060 and 1409 genes in PE vs controls and IUGR vs controls, respectively, many of those involved in placental function. While in IUGR placentas, alternative splicing affects genes specifically related to pregnancy, in preeclamptic placentas, it impacts a mix of genes related to pregnancy and brain diseases. Also, alternative splicing variations can be detected at the individual level as sharp splicing differences between different placentas. We correlate these variations with genetic variants to define splicing Quantitative Trait Loci (sQTL) in the subset of the 48 genes the most strongly alternatively spliced in placental diseases. We show that alternative splicing is at least partly piloted by genetic variants located either in cis (52 QTL identified) or in trans (52 QTL identified). In particular, we found four chromosomal regions that impact the splicing of genes in the placenta. The present work provides a new vision of placental gene expression regulation that warrants further studies.
Keyphrases
- genome wide
- gene expression
- dna methylation
- bioinformatics analysis
- genome wide identification
- preterm birth
- pregnancy outcomes
- endothelial cells
- blood pressure
- machine learning
- pregnant women
- genome wide analysis
- high glucose
- resting state
- high resolution
- transcription factor
- deep learning
- high density
- diabetic rats
- functional connectivity
- induced pluripotent stem cells