Mariner: explore the Hi-Cs.

MOTIVATION: 3D chromatin structure plays an important role in regulating gene expression and alterations to this structure can result in developmental abnormalities and disease. While genomic approaches like Hi-C and Micro-C can provide valuable insights in 3D chromatin architecture, the resulting datasets are extremely large and difficult to manipulate. RESULTS: Here, we present mariner, a rapid and memory efficient tool to extract, aggregate, and plot data from Hi-C matrices within the R/Bioconductor environment. Mariner simplifies the process of querying and extracting contacts from multiple Hi-C files using a parallel and block-processing approach. Modular functions allow complete workflow customization for advanced users, yet all-in-one functions are available for running the most common types of analyses. Finally, tight integration with existing Bioconductor infrastructure enables complete analysis and visualization of Hi-C data in R. AVAILABILITY AND IMPLEMENTATION: Available on GitHub at https://github.com/EricSDavis/mariner and on Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/mariner.html.

TDP-43 pathology links innate and adaptive immunity in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is the most common fatal motor neuron disease. Approximately 90% of ALS patients exhibit pathology of the master RNA regulator, Transactive Response DNA Binding protein (TDP-43). Despite the prevalence TDP-43 pathology in ALS motor neurons, recent findings suggest immune dysfunction is a determinant of disease progression in patients. Whether TDP-43 pathology elicits disease-modifying immune responses in ALS remains underexplored. In this study, we demonstrate that TDP-43 pathology is internalized by antigen presenting cells, causes vesicle rupture, and leads to innate and adaptive immune cell activation. Using a multiplex imaging platform, we observed interactions between innate and adaptive immune cells near TDP-43 pathological lesions in ALS brain. We used a mass cytometry-based whole-blood stimulation assay to provide evidence that ALS patient peripheral immune cells exhibit responses to TDP-43 aggregates. Taken together, this study provides a novel link between TDP-43 pathology and ALS immune dysfunction, and further highlights the translational and diagnostic implications of monitoring and manipulating the ALS immune response.

Guiding the design of well-powered Hi-C experiments to detect differential loops.

Motivation: Three-dimensional chromatin structure plays an important role in gene regulation by connecting regulatory regions and gene promoters. The ability to detect the formation and loss of these loops in various cell types and conditions provides valuable information on the mechanisms driving these cell states and is critical for understanding long-range gene regulation. Hi-C is a powerful technique for characterizing 3D chromatin structure; however, Hi-C can quickly become costly and labor-intensive, and proper planning is required to ensure efficient use of time and resources while maintaining experimental rigor and well-powered results. Results: To facilitate better planning and interpretation of human Hi-C experiments, we conducted a detailed evaluation of statistical power using publicly available Hi-C datasets, paying particular attention to the impact of loop size on Hi-C contacts and fold change compression. In addition, we have developed Hi-C Poweraid, a publicly hosted web application to investigate these findings. For experiments involving well-replicated cell lines, we recommend a total sequencing depth of at least 6 billion contacts per condition, split between at least two replicates to achieve the power to detect differences in the majority of loops. For experiments with higher variation, more replicates and deeper sequencing depths are required. Values for specific cases can be determined by using Hi-C Poweraid. This tool simplifies Hi-C power calculations, allowing for more efficient use of time and resources and more accurate interpretation of experimental results. Availability and implementation: Hi-C Poweraid is available as an R Shiny application deployed at http://phanstiel-lab.med.unc.edu/poweraid/, with code available at https://github.com/sarmapar/poweraid.

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants.

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

Chromatin loop dynamics during cellular differentiation are associated with changes to both anchor and internal regulatory features.

Three-dimensional (3D) chromatin structure has been shown to play a role in regulating gene transcription during biological transitions. Although our understanding of loop formation and maintenance is rapidly improving, much less is known about the mechanisms driving changes in looping and the impact of differential looping on gene transcription. One limitation has been a lack of well-powered differential looping data sets. To address this, we conducted a deeply sequenced Hi-C time course of megakaryocyte development comprising four biological replicates and 6 billion reads per time point. Statistical analysis revealed 1503 differential loops. Gained loop anchors were enriched for AP-1 occupancy and were characterized by large increases in histone H3K27ac (over 11-fold) but relatively small increases in CTCF and RAD21 binding (1.26- and 1.23-fold, respectively). Linear modeling revealed that changes in histone H3K27ac, chromatin accessibility, and JUN binding were better correlated with changes in looping than RAD21 and almost as well correlated as CTCF. Changes to epigenetic features between-rather than at-boundaries were highly predictive of changes in looping. Together these data suggest that although CTCF and RAD21 may be the core machinery dictating where loops form, other features (both at the anchors and within the loop boundaries) may play a larger role than previously anticipated in determining the relative loop strength across cell types and conditions.

Proximity-dependent recruitment of Polycomb repressive complexes by the lncRNA Airn.

During mouse embryogenesis, expression of the long non-coding RNA (lncRNA) Airn leads to gene repression and recruitment of Polycomb repressive complexes (PRCs) to varying extents over a 15-Mb domain. The mechanisms remain unclear. Using high-resolution approaches, we show in mouse trophoblast stem cells that Airn expression induces long-range changes to chromatin architecture that coincide with PRC-directed modifications and center around CpG island promoters that contact the Airn locus even in the absence of Airn expression. Intensity of contact between the Airn lncRNA and chromatin correlated with underlying intensity of PRC recruitment and PRC-directed modifications. Deletion of CpG islands that contact the Airn locus altered long-distance repression and PRC activity in a manner that correlated with changes in chromatin architecture. Our data imply that the extent to which Airn expression recruits PRCs to chromatin is controlled by DNA regulatory elements that modulate proximity of the Airn lncRNA product to its target DNA.

Chromatin alternates between A and B compartments at kilobase scale for subgenic organization.

Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF's RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted.

bootRanges: flexible generation of null sets of genomic ranges for hypothesis testing.

MOTIVATION: Enrichment analysis is a widely utilized technique in genomic analysis that aims to determine if there is a statistically significant association between two sets of genomic features. To conduct this type of hypothesis testing, an appropriate null model is typically required. However, the null distribution that is commonly used can be overly simplistic and may result in inaccurate conclusions. RESULTS: bootRanges provides fast functions for generation of block bootstrapped genomic ranges representing the null hypothesis in enrichment analysis. As part of a modular workflow, bootRanges offers greater flexibility for computing various test statistics leveraging other Bioconductor packages. We show that shuffling or permutation schemes may result in overly narrow test statistic null distributions and over-estimation of statistical significance, while creating new range sets with a block bootstrap preserves local genomic correlation structure and generates more reliable null distributions. It can also be used in more complex analyses, such as accessing correlations between cis-regulatory elements (CREs) and genes across cell types or providing optimized thresholds, e.g. log fold change (logFC) from differential analysis. AVAILABILITY AND IMPLEMENTATION: bootRanges is freely available in the R/Bioconductor package nullranges hosted at https://bioconductor.org/packages/nullranges.

excluderanges: exclusion sets for T2T-CHM13, GRCm39, and other genome assemblies.

SUMMARY: Exclusion regions are sections of reference genomes with abnormal pileups of short sequencing reads. Removing reads overlapping them improves biological signal, and these benefits are most pronounced in differential analysis settings. Several labs created exclusion region sets, available primarily through ENCODE and Github. However, the variety of exclusion sets creates uncertainty which sets to use. Furthermore, gap regions (e.g. centromeres, telomeres, short arms) create additional considerations in generating exclusion sets. We generated exclusion sets for the latest human T2T-CHM13 and mouse GRCm39 genomes and systematically assembled and annotated these and other sets in the excluderanges R/Bioconductor data package, also accessible via the BEDbase.org API. The package provides unified access to 82 GenomicRanges objects covering six organisms, multiple genome assemblies, and types of exclusion regions. For human hg38 genome assembly, we recommend hg38.Kundaje.GRCh38_unified_blacklist as the most well-curated and annotated, and sets generated by the Blacklist tool for other organisms. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/excluderanges/. Package website: https://dozmorovlab.github.io/excluderanges/.

matchRanges: generating null hypothesis genomic ranges via covariate-matched sampling.

MOTIVATION: Deriving biological insights from genomic data commonly requires comparing attributes of selected genomic loci to a null set of loci. The selection of this null set is non-trivial, as it requires careful consideration of potential covariates, a problem that is exacerbated by the non-uniform distribution of genomic features including genes, enhancers, and transcription factor binding sites. Propensity score-based covariate matching methods allow the selection of null sets from a pool of possible items while controlling for multiple covariates; however, existing packages do not operate on genomic data classes and can be slow for large data sets making them difficult to integrate into genomic workflows. RESULTS: To address this, we developed matchRanges, a propensity score-based covariate matching method for the efficient and convenient generation of matched null ranges from a set of background ranges within the Bioconductor framework. AVAILABILITY AND IMPLEMENTATION: Package: https://bioconductor.org/packages/nullranges, Code: https://github.com/nullranges, Documentation: https://nullranges.github.io/nullranges.

Guiding the design of well-powered Hi-C experiments to detect differential loops.

3D chromatin structure plays an important role in gene regulation by connecting regulatory regions and gene promoters. The ability to detect the formation and loss of these loops in various cell types and conditions provides valuable information on the mechanisms driving these cell states and is critical for understanding how long-range gene regulation works. Hi-C is a powerful technique used to characterize three-dimensional chromatin structure; however, Hi-C can quickly become a costly and labor-intensive endeavor, and proper planning is required to determine how to best use time and resources while maintaining experimental rigor and well-powered results. To facilitate better planning and interpretation of Hi-C experiments, we have conducted a detailed evaluation of statistical power using publicly available Hi-C datasets paying particular attention to the impact of loop size on Hi-C contacts and fold change compression. In addition, we have developed Hi-C Poweraid, a publicly-hosted web application to investigate these findings (http://phanstiel-lab.med.unc.edu/poweraid/). For experiments involving well-replicated cell lines, we recommend a total sequencing depth of at least 6 billion contacts per condition, split between at least 2 replicates in order to achieve the power to detect the majority of differential loops. For experiments with higher variation, more replicates and deeper sequencing depths are required. Exact values and recommendations for specific cases can be determined through the use of Hi-C Poweraid. This tool simplifies the complexities behind calculating power for Hi-C data and will provide useful information on the amount of well-powered loops an experiment will be able to detect given a specific set of experimental parameters, such as sequencing depth, replicates, and the sizes of the loops of interest. This will allow for more efficient use of time and resources and more accurate interpretation of experimental results.

Temporal analysis suggests a reciprocal relationship between 3D chromatin structure and transcription.

To infer potential causal relationships between 3D chromatin structure, enhancers, and gene transcription, we mapped each feature in a genome-wide fashion across eight narrowly spaced time points of macrophage activation. Enhancers and genes connected by loops exhibit stronger correlations between histone H3K27 acetylation and expression than can be explained by genomic distance or physical proximity alone. At these looped enhancer-promoter pairs, changes in acetylation at distal enhancers precede changes in gene expression. Changes in gene expression exhibit a directional bias at differential loop anchors; gained loops are associated with increased expression of genes oriented away from the center of the loop, and lost loops are often accompanied by high levels of transcription within the loop boundaries themselves. These results are consistent with a reciprocal relationship where loops can facilitate increased transcription by connecting promoters to distal enhancers, whereas high levels of transcription can impede loop formation.

3D chromatin structure in chondrocytes identifies putative osteoarthritis risk genes.

Genome-wide association studies have identified over 100 loci associated with osteoarthritis risk, but the majority of osteoarthritis risk variants are noncoding, making it difficult to identify the impacted genes for further study and therapeutic development. To address this need, we used a multiomic approach and genome editing to identify and functionally characterize potential osteoarthritis risk genes. Computational analysis of genome-wide association studies and ChIP-seq data revealed that chondrocyte regulatory loci are enriched for osteoarthritis risk variants. We constructed a chondrocyte-specific regulatory network by mapping 3D chromatin structure and active enhancers in human chondrocytes. We then intersected these data with our previously collected RNA-seq dataset of chondrocytes responding to fibronectin fragment, a known osteoarthritis trigger. Integration of the 3 genomic datasets with recently reported osteoarthritis genome-wide association study variants revealed a refined set of putative causal osteoarthritis variants and their potential target genes. One of the putative target genes identified was SOCS2, which was connected to a putative causal variant by a 170-kb loop and is differentially regulated in response to fibronectin fragment. CRISPR-Cas9-mediated deletion of SOCS2 in primary human chondrocytes from 3 independent donors led to heightened expression of inflammatory markers after fibronectin fragment treatment. These data suggest that SOCS2 plays a role in resolving inflammation in response to cartilage matrix damage and provides a possible mechanistic explanation for its influence on osteoarthritis risk. In total, we identified 56 unique putative osteoarthritis risk genes for further research and potential therapeutic development.

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer.

The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

Cell type-specific chromatin topology and gene regulation.

Chromatin structure is critically involved in gene regulation and cell fate determination. How this structure is established and maintained in distinct, terminally differentiated cells remains elusive. Winick-Ng et al. address this puzzle by applying immunoGAM in different brain cell types and reveal cell type-specific chromatin topologies, long gene decompaction, and the involvement of transcription factors (TFs).

Plotgardener: cultivating precise multi-panel figures in R.

MOTIVATION: The R programming language is one of the most widely used programming languages for transforming raw genomic datasets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. RESULTS: We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, esthetics and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. AVAILABILITY AND IMPLEMENTATION: Package: https://bioconductor.org/packages/plotgardener, Code: https://github.com/PhanstielLab/plotgardener, Documentation: https://phanstiellab.github.io/plotgardener/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CTCF: an R/bioconductor data package of human and mouse CTCF binding sites.

Summary: CTCF (CCCTC-binding factor) is an 11-zinc-finger DNA binding protein which regulates much of the eukaryotic genome's 3D structure and function. The diversity of CTCF binding motifs has led to a fragmented landscape of CTCF binding data. We collected position weight matrices of CTCF binding motifs and defined strand-oriented CTCF binding sites in the human and mouse genomes, including the recent Telomere to Telomere and mm39 assemblies. We included selected experimentally determined and predicted CTCF binding sites, such as CTCF-bound cis-regulatory elements from SCREEN ENCODE. We recommend filtering strategies for CTCF binding motifs and demonstrate that liftOver is a viable alternative to convert CTCF coordinates between assemblies. Our comprehensive data resource and usage recommendations can serve to harmonize and strengthen the reproducibility of genomic studies utilizing CTCF binding data. Availability and implementation: https://bioconductor.org/packages/CTCF. Companion website: https://dozmorovlab.github.io/CTCF/; Code to reproduce the analyses: https://github.com/dozmorovlab/CTCF.dev. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Phase separation drives aberrant chromatin looping and cancer development.

The development of cancer is intimately associated with genetic abnormalities that target proteins with intrinsically disordered regions (IDRs). In human haematological malignancies, recurrent chromosomal translocation of nucleoporin (NUP98 or NUP214) generates an aberrant chimera that invariably retains the nucleoporin IDR-tandemly dispersed repeats of phenylalanine and glycine residues1,2. However, how unstructured IDRs contribute to oncogenesis remains unclear. Here we show that IDRs contained within NUP98-HOXA9, a homeodomain-containing transcription factor chimera recurrently detected in leukaemias1,2, are essential for establishing liquid-liquid phase separation (LLPS) puncta of chimera and for inducing leukaemic transformation. Notably, LLPS of NUP98-HOXA9 not only promotes chromatin occupancy of chimera transcription factors, but also is required for the formation of a broad 'super-enhancer'-like binding pattern typically seen at leukaemogenic genes, which potentiates transcriptional activation. An artificial HOX chimera, created by replacing the phenylalanine and glycine repeats of NUP98 with an unrelated LLPS-forming IDR of the FUS protein3,4, had similar enhancing effects on the genome-wide binding and target gene activation of the chimera. Deeply sequenced Hi-C revealed that phase-separated NUP98-HOXA9 induces CTCF-independent chromatin loops that are enriched at proto-oncogenes. Together, this report describes a proof-of-principle example in which cancer acquires mutation to establish oncogenic transcription factor condensates via phase separation, which simultaneously enhances their genomic targeting and induces organization of aberrant three-dimensional chromatin structure during tumourous transformation. As LLPS-competent molecules are frequently implicated in diseases1,2,4-7, this mechanism can potentially be generalized to many malignant and pathological settings.

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration.

Genome-wide association studies (GWAS) have successfully identified hundreds of genomic loci that are associated with human traits and disease. However, because the majority of the genome-wide significant (GWS) loci fall onto the non-coding genome, the functional impact of many remain unknown. Three-dimensional chromatin interactions identified by Hi-C or its derivatives can provide useful tools to annotate these loci by linking non-coding variants to their actionable genes. Here, we outline a protocol to map GWAS non-coding variants to their putative genes using Alzheimer's disease (AD) GWAS and Hi-C datasets from human adult brain tissue. Putative causal single-nucleotide polymorphisms (SNPs) are identified by application of fine-mapping algorithms. SNPs are then mapped to their putative target genes using enhancer-promoter interactions based on Hi-C. The resulting gene set represents AD risk genes, as they are potentially regulated by AD risk variants. To garner further biological insights into molecular mechanisms underlying AD, we characterize AD risk genes using developmental brain expression data and brain single-cell expression profiles. This protocol can be expanded to any GWAS and Hi-C datasets to identify putative target genes and molecular mechanisms underlying various human traits and diseases.

Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements.

Pooled CRISPR-Cas9 screens are a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we investigate Cas9, dCas9, and CRISPRi/a off-target activity in screens for essential regulatory elements. The sgRNAs with the largest effects in genome-scale screens for essential CTCF loop anchors in K562 cells were not single guide RNAs (sgRNAs) that disrupted gene expression near the on-target CTCF anchor. Rather, these sgRNAs had high off-target activity that, while only weakly correlated with absolute off-target site number, could be predicted by the recently developed GuideScan specificity score. Screens conducted in parallel with CRISPRi/a, which do not induce double-stranded DNA breaks, revealed that a distinct set of off-targets also cause strong confounding fitness effects with these epigenome-editing tools. Promisingly, filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and enabled identification of essential regulatory elements.