Aslihan Karabacak

More than twenty years ago, the FANTOM consortium uncovered that mammalian genomes are pervasively transcribed, revealing multitudes of RNAs with unknown functions. A subset of these transcripts has since then been linked to transcriptional control and to chromatin organization via their ability to interact with DNA, suggesting that chromatin-associated RNAs could be key players in genome regulation. Although recent technological advances now enable the mapping of genome-wide RNA-DNA contacts, a lack of analyses integrating these methods with other genomic features and across multiple cellular contexts hinders our comprehensive understanding of the principles underlying RNA-DNA interactions and of their biological importance. As part of the FANTOM6 project, we thus generated RNA-DNA interaction maps in 16 different human cell types, then combined these contacts with multiple layers of other genomic data to investigate how patterns of interaction between RNA and DNA relate to chromatin organization and function. We show that the RNA-DNA interactome is highly dynamic yet reproducibly organized in cell-type specific networks, constituted of a great diversity of interactions that vary in function of their distance, the nature of their sources and the chromatin state of their targets. In particular, we detected numerous regulatory elements that exhibit marked changes in activity when differentially bound by transcripts, implying that thousands of RNA-DNA interactions can play a mechanistic role in gene expression. This regulatory function correlates with RNA-protein interactions and significantly associates with cell type-relevant and disorder-related traits. In addition to providing essential resources for future research in RNA-mediated chromatin regulation, cellular biology and human diseases, our study thus establishes the RNA-DNA interactome as a new genome regulatory layer that defines and maintains cellular identity and behavior.

Noncoding RNAs (ncRNAs) regulate gene expression through RNA–chromatin interactions, yet their role in human brain evolution remains unclear. To map these interactions, we integrated FANTOM6 neurogenic data with a comprehensive atlas of regulatory regions active during early cortical development. This integration effort linked ncRNAs and transcription factors (TFs) to their genomic targets, revealing functional interactions captured by RNA-DNA contact mapping, yet largely missed by chromatin conformation data. Overlaying these interactions with nearly fixed variants in Homo sapiens relative to extinct hominins uncovered widespread rewiring of TF binding, affecting genes implicated in progenitor proliferation and neuronal differentiation. Gene regulatory network reconstruction identified TEAD2 and ONECUT2 as key regulators of intermediate progenitors and migrating neurons. Functional perturbation followed by Cut&Tag and ATAC-seq linked the two TFs to disease- and evolutionarily- relevant pathways. Together, these results show how ncRNA–TF cooperation and selective cis -regulatory rewiring contributed to sapiens-specific features of cortical development.

Fibroblasts are non-hematopoietic structural cells that define the architecture of organs, support the homeostasis of tissue-resident cells and play key roles in fibrosis, cancer, autoimmunity and wound healing. Recent studies have described fibroblast heterogeneity within individual tissues. However, the field lacks a definition of fibroblasts at single-cell resolution across tissues in healthy and diseased organs. Here, we integrated single-cell RNA transcriptomic data from ~150,000 fibroblast cells derived from 16 steady- and 11 perturbed-state mouse organs into fibroblast atlases. These data revealed two universal fibroblast cell subtypes, marked by expression of Pi16 or Col15a1 , in all tissues; it also revealed discrete subsets of five specialized fibroblast subtypes in steady-state tissues and three activated fibroblast subtypes in perturbed or diseased tissues. These subsets were transcriptionally shaped by microenvironmental context rather than tissue-type alone. Inference of fibroblast lineage structure from the murine steady-state and perturbed-state fibroblast atlases suggested that specialized and activated subtypes are developmentally related to universal tissue-resident fibroblasts. Analysis of human samples revealed that fibroblast subtypes found in mice are conserved between species, including universal fibroblasts and activated phenotypes associated with pathogenicity in human cancer, fibrosis, arthritis and inflammation. In sum, a cross-species and pan-tissue approach to transcriptomics at single-cell resolution enabled us to define the organizing principles of the fibroblast lineage in health and disease.

DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints. We demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq.

Divergent transcription from promoters and enhancers is pervasive in many species, but it remains unclear if it is a general feature of all eukaryotic cis regulatory elements. To address this, here we define cis regulatory elements in C. elegans, D. melanogaster and H. sapiens and investigate the determinants of their transcription directionality. In all three species, we find that divergent transcription is initiated from two separate core promoter sequences and promoter regions display competition between histone modifications on the + 1 and −1 nucleosomes. In contrast, promoter directionality, sequence composition surrounding promoters, and positional enrichment of chromatin states, are different across species. Integrative models of H3K4me3 levels and core promoter sequence are highly predictive of promoter and enhancer directionality and support two directional classes, skewed and balanced. The relative importance of features to these models are clearly distinct for promoters and enhancers. Differences in regulatory architecture within and between metazoans are therefore abundant, arguing against a unified eukaryotic model.

DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBS) in regulatory regions via footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impacts the discrimination of footprint from background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.

Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73–98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.