Enhancer grammar of liver cell types and hepatocyte zonation states

Abstract

Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs across liver cell types at high resolution, using a combination of single cell multiomics, spatial omics, GRN inference, and deep learning. We found that cell state changes in transcription and chromatin accessibility in hepatocytes, liver sinusoidal endothelial cells and hepatic stellate cells depend on zonation. Enhancer-GRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo massively parallel reporter assay on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and enhancer assays, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal understanding of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with specific activity levels and zonation patterns.

An extended transcription factor regulatory network controls hepatocyte identity

“…For instance, Hep‐ID CONNECT TFs comprise regulators of rhythmic gene expression including XBP1. Other Hep‐ID CONNECT TFs, TBX3 and TCF7L2, have been identified as regulators of zonated hepatocyte transcriptional programs (preprint: González‐Blas et al , 2022 ) pointing to a role of Hep‐ID CONNECT TFs in specifying proper hepatic gene expression in both space and time. Importantly, rhythmic and zonated hepatic gene expression are crucial to maintain appropriate liver metabolic and non‐metabolic activities (Mukherji et al , 2019 ; Meng et al , 2020 ; Pan et al , 2020 ; Paris & Henderson, 2022 ).…”

Transcription factor interactions explain the context-dependent activity of CRX binding sites

“…A recent general-purpose modeling framework, called MAVE-NN, overcomes these challenges using a neural-network based approach to fit interpretable genotype-phenotype maps to data from massively parallel functional assays [57]. An important difference between MAVE-NN and recent deep learning models such as Deep-STARR and others [58][59][60][61] is that MAVE-NN explicitly models the relationship between sequence and activity separately from features of the experimental measurement, such as saturation, detection limits, and noise, rather than attempting to model all features of an MPRA dataset in a monolithic architecture trained end-to-end. This enables MAVE-NN models to learn interpretable parameters that correspond straightforwardly to additive contributions and interactions between sequence features such as TFBSs.…”

Active learning of enhancer and silencer regulatory grammar in photoreceptors

“…Machine learning presents an opportunity to train better models of cis-regulatory grammars, due to its power to discover predictive features in high dimensional data. Deep neural network models trained on large epigenomic datasets often predict TF binding and chromatin accessibility with high accuracy, and these models have revealed important contextual features of local DNA sequence that determine TF binding (25)(26)(27)(28)(29)(30)(31)(32). However, models trained on massively parallel reporter gene assays (MPRAs) to predict CRE activity (20,(33)(34)(35)(36)(37)(38)(39)(40) often perform less well than binding models, likely because the cis-regulatory grammars that govern activity depend on additional higher-order interactions between bound TFs and their associated co-factors (2,(4)(5)(6)41).…”

“…Modern machine learning methods, with their power to discover predictive features in high dimensional data, have the potential to discover features of cis-regulatory grammars that are missed by conventional enrichment analyses of TF motifs. Several studies report machine learning models trained on large epigenomic datasets [24][25][26][27][28][29][30][31] . These studies attempt to learn the entire cis-regulatory grammar of a cell type.…”