Dynamics of transcriptional enhancers and chromosome topology in gene regulation

GEEES: inferring cell-specific gene-enhancer interactions from multi-modal single-cell data

MOTIVATION

Gene-enhancer interactions are central to transcriptional regulation. Current multi-modal single-cell datasets that profile transcriptome and chromatin accessibility simultaneously in a single cell are yielding opportunities to infer gene-enhancer associations in a cell type specific manner. Computational efforts for such multi-modal single-cell datasets thus far focused on methods for identification and refinement of cell types and trajectory construction. While initial attempts for inferring gene-enhancer interactions have emerged, these have not been evaluated against benchmark datasets that materialized from bulk genomic experiments. Furthermore, existing approaches are limited to inferring gene-enhancer associations at the level of grouped cells as opposed to individual cells, thereby ignoring regulatory heterogeneity among the cells.

RESULTS

We present a new approach, GEEES for "Gene EnhancEr IntEractions from Multi-modal Single Cell Data," for inferring gene-enhancer associations at the single-cell level using multi-modal single-cell transcriptome and chromatin accessibility data. We evaluated GEEES alongside several multivariate regression-based alternatives we devised and state-of-the-art methods using a large number of benchmark datasets, providing a comprehensive assessment of current approaches. This analysis revealed significant discrepancies between gold-standard interactions and gene-enhancer associations derived from multi-modal single-cell data. Notably, incorporating gene-enhancer distance into the analysis markedly improved performance across all methods, positioning GEEES as a leading approach in this domain. While the overall improvement in performance metrics by GEEES is modest, it provides enhanced cell representation learning which can be leveraged for more effective downstream analysis. Furthermore, our review of existing experimentally driven benchmark datasets uncovers their limited concordance, underscoring the necessity for new high-throughput experiments to validate gene-enhancer interactions inferred from single-cell data.

AVAILABILITY AND IMPLEMENTATION

https://github.com/keleslab/GEEES.

Chromosome structure modeling tools and their evaluation in bacteria

The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.

Distinct roles for CKM-Mediator in controlling Polycomb-dependent chromosomal interactions and priming genes for induction

Precise control of gene expression underpins normal development. This relies on mechanisms that enable communication between gene promoters and other regulatory elements. In embryonic stem cells (ESCs), the cyclin-dependent kinase module Mediator complex (CKM-Mediator) has been reported to physically link gene regulatory elements to enable gene expression and also prime genes for induction during differentiation. Here, we show that CKM-Mediator contributes little to three-dimensional genome organization in ESCs, but it has a specific and essential role in controlling interactions between inactive gene regulatory elements bound by Polycomb repressive complexes (PRCs). These interactions are established by the canonical PRC1 (cPRC1) complex but rely on CKM-Mediator, which facilitates binding of cPRC1 to its target sites. Importantly, through separation-of-function experiments, we reveal that this collaboration between CKM-Mediator and cPRC1 in creating long-range interactions does not function to prime genes for induction during differentiation. Instead, we discover that priming relies on an interaction-independent mechanism whereby the CKM supports core Mediator engagement with gene promoters during differentiation to enable gene activation.

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology - namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

Computational Screening of Phase-separating Proteins

Phase separation is an important mechanism that mediates the compartmentalization of proteins in cells. Proteins that can undergo phase separation in cells share certain typical sequence features, like intrinsically disordered regions (IDRs) and multiple modular domains. Sequence-based analysis tools are commonly used in the screening of these proteins. However, current phase separation predictors are mostly designed for IDR-containing proteins, thus inevitably overlook the phase-separating proteins with relatively low IDR content. Features other than amino acid sequence could provide crucial information for identifying possible phase-separating proteins: protein–protein interaction (PPI) networks show multivalent interactions that underlie phase separation process; post-translational modifications (PTMs) are crucial in the regulation of phase separation behavior; spherical structures revealed in immunofluorescence (IF)images indicate condensed droplets formed by phase-separating proteins, distinguishing these proteins from non-phase-separating proteins. Here, we summarize the sequence-based tools for predicting phase-separating proteins and highlight the importance of incorporating PPIs, PTMs, and IF images into phase separation prediction in future studies.

Independence of chromatin conformation and gene regulation during Drosophila dorsoventral patterning

The relationship between chromatin organization and gene regulation remains unclear. While disruption of chromatin domains and domain boundaries can lead to misexpression of developmental genes, acute depletion of regulators of genome organization has a relatively small effect on gene expression. It is therefore uncertain whether gene expression and chromatin state drive chromatin organization or whether changes in chromatin organization facilitate cell-type-specific activation of gene expression. Here, using the dorsoventral patterning of the Drosophila melanogaster embryo as a model system, we provide evidence for the independence of chromatin organization and dorsoventral gene expression. We define tissue-specific enhancers and link them to expression patterns using single-cell RNA-seq. Surprisingly, despite tissue-specific chromatin states and gene expression, chromatin organization is largely maintained across tissues. Our results indicate that tissue-specific chromatin conformation is not necessary for tissue-specific gene expression but rather acts as a scaffold facilitating gene expression when enhancers become active.

The 3D Genome as a Target for Anticancer Therapy

The role of 3D genome organization in the precise regulation of gene expression is well established. Accordingly, the mechanistic connections between 3D genome alterations and disease development are becoming increasingly apparent. This opinion article provides a snapshot of our current understanding of the 3D genome alterations associated with cancers. We discuss potential connections of the 3D genome and cancer transcriptional addiction phenomenon as well as molecular mechanisms of action of 3D genome-disrupting drugs. Finally, we highlight issues and perspectives raised by the discovery of the first pharmaceutical strongly affecting 3D genome organization.

The Conserved Non-Coding Sequence 2 (CNS2) Enhances CD69 Transcription through Cooperation between the Transcription Factors Oct1 and RUNX1

The immune regulatory receptor CD69 is expressed upon activation in all types of leukocytes and is strongly regulated at the transcriptional level. We previously described that, in addition to the CD69 promoter, there are four conserved noncoding regions (CNS1-4) upstream of the CD69 promoter. Furthermore, we proposed that CNS2 is the main enhancer of CD69 transcription. In the present study, we mapped the transcription factor (TF) binding sites (TFBS) from ChIP-seq databases within CNS2. Through luciferase reporter assays, we defined a ~60 bp sequence that acts as the minimum enhancer core of mouse CNS2, which includes the Oct1 TFBS. This enhancer core establishes cooperative interactions with the 3′ and 5′ flanking regions, which contain RUNX1 BS. In agreement with the luciferase reporter data, the inhibition of RUNX1 and Oct1 TF expression by siRNA suggests that they synergistically enhance endogenous CD69 gene transcription. In summary, we describe an enhancer core containing RUNX1 and Oct1 BS that is important for the activity of the most potent CD69 gene transcription enhancer.

Dynamics of transcriptional enhancers and chromosome topology in gene regulation

Transcriptional enhancers are regulatory DNAs that instruct when and where genes should be transcribed in response to a variety of intrinsic and external signals. They contain a cluster of binding sites for sequence-specific transcription factors and co-activators to determine the spatiotemporal specificity of gene activities during development. Enhancers are often positioned in distal locations from their target promoters. In some cases, they work over a million base pairs or more. In the traditional view, enhancers have been thought to stably interact with promoters in a targeted manner. However, quantitative imaging studies provide a far more dynamic picture of enhancer action. Moreover, recent Hi-C methods suggest that regulatory interactions are dynamically regulated by the higher-order chromosome topology. In this review, we summarize the emerging findings in the field and propose that assembly of "transcription hubs" in the context of 3D genome structure plays an important role in transcriptional regulation.