General model with biclustering for target selection in severe preeclampsia

Abstract

Preeclampsia is a complex and multifactorial disease and a leading cause of death in pregnancy with no current effective treatment strategies, especially severe preeclampsia. A new general regression model with biclustering scalability is developed to quantify causal relationship from genes to gene products to clinical phenotypes among multiple datasets. It can identify those key genes and relationships between gene expression and clinical phenotype, which will be interpreted further, for molecular mechanism disclosure and effective drug development by pathway enrichment analysis. We observed pattern variation of gene expression relating to clinical phenotype among three preeclampsia datasets in parallel. Eighty-one genes are recommended as potential druggable targets for severe preeclampsia by their highly correlation between gene expression variation with preeclampsia clinical phenotypes in hypertension, HELLP (Hemolysis, Elevated Liver enzymes and Low Platelets) syndrome, proteinuria, and gestation week. Genes ENG, Flt and NEU1 and NEU2 show overexpression in severe preeclampsia in attenuating hypertension and proteinuria in multiple datasets of preeclampsia. Those genes are involved in the angiogenic factors and vascular molecular function. Treatment with either Flt or NEU1 have exciting clinical implications, and likely will transform the detection and treatment for patients with severe preeclampsia. The novel network mathematical models can quantify a causal relationship from genes to gene products to clinical phenotypes, and they can model causal influences of genes that are either monomorphic or polymorphic among heterology datasets, which is suggesting that key gene expression variation may be more likely to influence complex clinical phenotype variation in preeclampsia among multiple datasets.