Levine, R. | bioRxiv Science

bioRxiv2018

A human cell model of cardiac pathophysiological valvulogenesis

Genetically modified mice have advanced our understanding of valve development and related pathologies. Yet, little is known regarding human valvulogenesis in health and diseases. Genuine human in vitro models that reproduce valvular (patho)biology are thus needed. We here developed a human pluripotent stem cell-derived model fit to decode the early steps of human valvulogenesis and to recapitulate valve disease traits in a dish.\n\nUsing cellular based, single cell omics-informed and in vivo-validated approaches, we derived a population of pre-valvular endocardial cells from a pluripotent stem cell source. These human prevalvular cells (HPVCs) expressed gene patterns conforming to the atrio-ventricular canal (AVC) endocardium signature originally established in E9.0 mouse embryos. In fact, HPVC treated with BMP2, cultured onto mouse AVC cushions, or transplanted into the AVC of embryonic mouse hearts, underwent endothelial-to-mesenchymal transition and expressed markers of valve interstitial cells of different valvular layers demonstrating tissue functionality. HPVCs also differentiated into tendinous/chondrogenic cells in line with the valvular repertoire. Extending this valvulogenic model to patient specific iPS cells, we recapitulated features of mitral valve prolapse and uncovered that dysregulation of the SHH pathway is likely to be at the origin of the disease thus providing a putative therapeutic target.\n\nHuman pluripotent stem cells recapitulate early valvulogenesis and provide a powerful model to systematically decipher the origin and lineage contribution of different valvular cell types in humans as well as to study valve diseases in a dish.

developmental biology↗

bioRxiv2017

Semi-Parametric Covariate-Modulated Local False Discovery Rate For Genome-Wide Association Studies

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p [≤] 5 x 10-8. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr [≤] 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

bioinformatics↗