Differential contribution to gene expression prediction of histone modifications at enhancers or promoters

• Published in: PLoS computational biology Vol. 17; no. 9; p. e1009368
• Main Authors: González-Ramírez, Mar, Ballaré, Cecilia, Mugianesi, Francesca, Beringer, Malte, Santanach, Alexandra, Blanco, Enrique, Di Croce, Luciano
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.09.2021; Public Library of Science (PLoS)
• Subjects: Animals; Biology and Life Sciences; Cell Differentiation - genetics; Cells, Cultured; Chromatin Immunoprecipitation Sequencing - statistics & numerical data; Computational Biology; Differentiation; Embryo cells; Enhancer Elements, Genetic; Enhancers; Epigenetics; Experiments; Gene Expression; Genetic aspects; Genomes; Histone Code - genetics; Histones; Humans; Mice; Models, Genetic; Mouse Embryonic Stem Cells - metabolism; Physical Sciences; Physiological aspects; Prediction models; Promoter Regions, Genetic; Promoters; Regression Analysis; Research and Analysis Methods; Stem cells; Spain
• DOI: 10.1371/journal.pcbi.1009368
• ISSN: 1553-7358; 1553-734X

The ChIP-seq signal of histone modifications at promoters is a good predictor of gene expression in different cellular contexts, but whether this is also true at enhancers is not clear. To address this issue, we develop quantitative models to characterize the relationship of gene expression with histone modifications at enhancers or promoters. We use embryonic stem cells (ESCs), which contain a full spectrum of active and repressed (poised) enhancers, to train predictive models. As many poised enhancers in ESCs switch towards an active state during differentiation, predictive models can also be trained on poised enhancers throughout differentiation and in development. Remarkably, we determine that histone modifications at enhancers, as well as promoters, are predictive of gene expression in ESCs and throughout differentiation and development. Importantly, we demonstrate that their contribution to the predictive models varies depending on their location in enhancers or promoters. Moreover, we use a local regression (LOESS) to normalize sequencing data from different sources, which allows us to apply predictive models trained in a specific cellular context to a different one. We conclude that the relationship between gene expression and histone modifications at enhancers is universal and different from promoters. Our study provides new insight into how histone modifications relate to gene expression based on their location in enhancers or promoters.