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.

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.

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/.

SUPPLEMENTARY INFORMATION

Package website: https://dozmorovlab.github.io/excluderanges/.

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.

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.

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).

Synthetic amyloid beta does not induce a robust transcriptional response in innate immune cell culture systems

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease that impacts nearly 400 million people worldwide. The accumulation of amyloid beta (Aβ) in the brain has historically been associated with AD, and recent evidence suggests that neuroinflammation plays a central role in its origin and progression. These observations have given rise to the theory that Aβ is the primary trigger of AD, and induces proinflammatory activation of immune brain cells (i.e., microglia), which culminates in neuronal damage and cognitive decline. To test this hypothesis, many in vitro systems have been established to study Aβ-mediated activation of innate immune cells. Nevertheless, the transcriptional resemblance of these models to the microglia in the AD brain has never been comprehensively studied on a genome-wide scale.

METHODS

We used bulk RNA-seq to assess the transcriptional differences between in vitro cell types used to model neuroinflammation in AD, including several established, primary and iPSC-derived immune cell lines (macrophages, microglia and astrocytes) and their similarities to primary cells in the AD brain. We then analyzed the transcriptional response of these innate immune cells to synthetic Aβ or LPS and INFγ.

RESULTS

We found that human induced pluripotent stem cell (hIPSC)-derived microglia (IMGL) are the in vitro cell model that best resembles primary microglia. Surprisingly, synthetic Aβ does not trigger a robust transcriptional response in any of the cellular models analyzed, despite testing a wide variety of Aβ formulations, concentrations, and treatment conditions. Finally, we found that bacterial LPS and INFγ activate microglia and induce transcriptional changes that resemble many, but not all, aspects of the transcriptomic profiles of disease associated microglia (DAM) present in the AD brain.

CONCLUSIONS

These results suggest that synthetic Aβ treatment of innate immune cell cultures does not recapitulate transcriptional profiles observed in microglia from AD brains. In contrast, treating IMGL with LPS and INFγ induces transcriptional changes similar to those observed in microglia detected in AD brains.

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.

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.

Transcriptional response of human articular chondrocytes treated with fibronectin fragments: an in vitro model of the osteoarthritis phenotype

OBJECTIVE

Fibronectin is a matrix protein that is fragmented during cartilage degradation in osteoarthritis (OA). Treatment of chondrocytes with fibronectin fragments (FN-f) has been used to model OA in vitro, but the system has not been fully characterized. This study sought to define the transcriptional response of chondrocytes to FN-f, and directly compare it to responses traditionally observed in OA.

DESIGN

Normal human femoral chondrocytes isolated from tissue donors were treated with either FN-f or PBS (control) for 3, 6, or 18 h. RNA-seq libraries were compared between time-matched FN-f and control samples in order to identify changes in gene expression over time. Differentially expressed genes were compared to a published OA gene set and used for pathway, transcription factor motif, and kinome analysis.

RESULTS

FN-f treatment resulted in 3,914 differentially expressed genes over the time course. Genes that are up- or downregulated in OA were significantly up- (P < 0.00001) or downregulated (P < 0.0004) in response to FN-f. Early response genes were involved in proinflammatory pathways, whereas many late response genes were involved in ferroptosis. The promoters of upregulated genes were enriched for NF-κB, AP-1, and IRF motifs. Highly upregulated kinases included CAMK1G, IRAK2, and the uncharacterized kinase DYRK3, while growth factor receptors TGFBR2 and FGFR2 were downregulated.

CONCLUSIONS

FN-f treatment of normal human articular chondrocytes recapitulated many key aspects of the OA chondrocyte phenotype. This in vitro model is promising for future OA studies, especially considering its compatibility with genomics and genome-editing techniques.

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.

Bedtoolsr: An R package for genomic data analysis and manipulation

The sequencing of the human genome and subsequent advances in DNA sequencing technology have created a need for computational tools to analyze and manipulate genomic data sets. The bedtools software suite and the R programming language have emerged as indispensable tools for this purpose but have lacked integration. Here we describe bedtoolsr, an R package that provides simple and intuitive access to all bedtools functions from within the R programming environment. We provide several usability enhancements, support compatibility with past and future versions of bedtools, and include unit tests to ensure stability. bedtoolsr provides a user-focused, harmonious integration of the bedtools software suite with the R programming language that should be of great use to the genomics community.

CoralP: Flexible visualization of the human phosphatome

Protein phosphatases and kinases play critical roles in a host of biological processes and diseases via the removal and addition of phosphoryl groups. While kinases have been extensively studied for decades, recent findings regarding the specificity and activities of phosphatases have generated an increased interest in targeting phosphatases for pharmaceutical development. This increased focus has created a need for methods to visualize this important class of proteins within the context of the entire phosphatase protein family. Here, we present CoralP, an interactive web application for the generation of customizable, publication-quality representations of human phosphatome data. Phosphatase attributes can be encoded through edge colors, node colors, and node sizes. CoralP is the first and currently the only tool designed for phosphatome visualization and should be of great use to the signaling community. Source Code: https://github.com/PhanstielLab/coralp Web Application: http://phanstiel-lab.med.unc.edu/coralp.

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.

Coral: Clear and Customizable Visualization of Human Kinome Data

Protein kinases represent one of the largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive web application for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branch color), allows three modes of kinome visualization (the traditional kinome tree as well as radial and dynamic force networks), and generates high-resolution scalable vector graphics files suitable for publication without the need for refinement using graphics editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/CORAL and phanstiel-lab.med.unc.edu/Coral, respectively.

Topological organization and dynamic regulation of human tRNA genes during macrophage differentiation

Background

The human genome is hierarchically organized into local and long-range structures that help shape cell-type-specific transcription patterns. Transfer RNA (tRNA) genes (tDNAs), which are transcribed by RNA polymerase III (RNAPIII) and encode RNA molecules responsible for translation, are dispersed throughout the genome and, in many cases, linearly organized into genomic clusters with other tDNAs. Whether the location and three-dimensional organization of tDNAs contribute to the activity of these genes has remained difficult to address, due in part to unique challenges related to tRNA sequencing. We therefore devised integrated tDNA expression profiling, a method that combines RNAPIII mapping with biotin-capture of nascent tRNAs. We apply this method to the study of dynamic tRNA gene regulation during macrophage development and further integrate these data with high-resolution maps of 3D chromatin structure.

Results

Integrated tDNA expression profiling reveals domain-level and loop-based organization of tRNA gene transcription during cellular differentiation. tRNA genes connected by DNA loops, which are proximal to CTCF binding sites and expressed at elevated levels compared to non-loop tDNAs, change coordinately with tDNAs and protein-coding genes at distal ends of interactions mapped by in situ Hi-C. We find that downregulated tRNA genes are specifically marked by enhanced promoter-proximal binding of MAF1, a transcriptional repressor of RNAPIII activity, altogether revealing multiple levels of tDNA regulation during cellular differentiation.

Conclusions

We present evidence of both local and coordinated long-range regulation of human tDNA expression, suggesting the location and organization of tRNA genes contribute to dynamic tDNA activity during macrophage development.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1310-3) contains supplementary material, which is available to authorized users.

Mango: a bias-correcting ChIA-PET analysis pipeline

MOTIVATION

Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) is an established method for detecting genome-wide looping interactions at high resolution. Current ChIA-PET analysis software packages either fail to correct for non-specific interactions due to genomic proximity or only address a fraction of the steps required for data processing. We present Mango, a complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity.

RESULTS

Comparison to the existing software packages, ChIA-PET Tool and ChiaSig revealed that Mango interactions exhibit much better agreement with high-resolution Hi-C data. Importantly, Mango executes all steps required for processing ChIA-PET datasets, whereas ChiaSig only completes 20% of the required steps. Application of Mango to multiple available ChIA-PET datasets permitted the independent rediscovery of known trends in chromatin loops including enrichment of CTCF, RAD21, SMC3 and ZNF143 at the anchor regions of interactions and strong bias for convergent CTCF motifs.

AVAILABILITY AND IMPLEMENTATION

Mango is open source and distributed through github at https://github.com/dphansti/mango.

CONTACT

mpsnyder@standford.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-wide map of regulatory interactions in the human genome

Increasing evidence suggests that interactions between regulatory genomic elements play an important role in regulating gene expression. We generated a genome-wide interaction map of regulatory elements in human cells (ENCODE tier 1 cells, K562, GM12878) using Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET) experiments targeting six broadly distributed factors. Bound regions covered 80% of DNase I hypersensitive sites including 99.7% of TSS and 98% of enhancers. Correlating this map with ChIP-seq and RNA-seq data sets revealed cohesin, CTCF, and ZNF143 as key components of three-dimensional chromatin structure and revealed how the distal chromatin state affects gene transcription. Comparison of interactions between cell types revealed that enhancer-promoter interactions were highly cell-type-specific. Construction and comparison of distal and proximal regulatory networks revealed stark differences in structure and biological function. Proximal binding events are enriched at genes with housekeeping functions, while distal binding events interact with genes involved in dynamic biological processes including response to stimulus. This study reveals new mechanistic and functional insights into regulatory region organization in the nucleus.

Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures

Motivation: Interpretation and communication of genomic data require flexible and quantitative tools to analyze and visualize diverse data types, and yet, a comprehensive tool to display all common genomic data types in publication quality figures does not exist to date. To address this shortcoming, we present Sushi.R, an R/Bioconductor package that allows flexible integration of genomic visualizations into highly customizable, publication-ready, multi-panel figures from common genomic data formats including Browser Extensible Data (BED), bedGraph and Browser Extensible Data Paired-End (BEDPE). Sushi.R is open source and made publicly available through GitHub (https://github.com/dphansti/Sushi) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/Sushi.html).

Contact:mpsnyder@stanford.edu or dphansti@stanford.edu

Whole-exome sequencing identifies tetratricopeptide repeat domain 7A (TTC7A) mutations for combined immunodeficiency with intestinal atresias

BACKGROUND

Combined immunodeficiency with multiple intestinal atresias (CID-MIA) is a rare hereditary disease characterized by intestinal obstructions and profound immune defects.

OBJECTIVE

We sought to determine the underlying genetic causes of CID-MIA by analyzing the exomic sequences of 5 patients and their healthy direct relatives from 5 unrelated families.

METHODS

We performed whole-exome sequencing on 5 patients with CID-MIA and 10 healthy direct family members belonging to 5 unrelated families with CID-MIA. We also performed targeted Sanger sequencing for the candidate gene tetratricopeptide repeat domain 7A (TTC7A) on 3 additional patients with CID-MIA.

RESULTS

Through analysis and comparison of the exomic sequence of the subjects from these 5 families, we identified biallelic damaging mutations in the TTC7A gene, for a total of 7 distinct mutations. Targeted TTC7A gene sequencing in 3 additional unrelated patients with CID-MIA revealed biallelic deleterious mutations in 2 of them, as well as an aberrant splice product in the third patient. Staining of normal thymus showed that the TTC7A protein is expressed in thymic epithelial cells, as well as in thymocytes. Moreover, severe lymphoid depletion was observed in the thymus and peripheral lymphoid tissues from 2 patients with CID-MIA.

CONCLUSIONS

We identified deleterious mutations of the TTC7A gene in 8 unrelated patients with CID-MIA and demonstrated that the TTC7A protein is expressed in the thymus. Our results strongly suggest that TTC7A gene defects cause CID-MIA.

Proteomics and pluripotency

The fields of mass spectrometry (MS) and stem cell biology have expanded greatly in the past twenty years. Taken alone, these fields occupy entirely different branches of science; however, the points where they overlap provide valuable insight, both in the biological and technical arenas. From a biological perspective, MS-based proteomics offers the capacity to follow post-transcriptional regulation and signaling that are (1) fundamental to pluripotency and differentiation, (2) largely beyond the reach of genomic technologies, and (3) otherwise difficult or impossible to examine on a large scale. At the same time, addressing questions fundamental to stem cell biology has compelled proteomic researchers to pursue more sensitive and creative ways to probe the proteome, both in a targeted and high-throughput manner. Here, we highlight experiments that straddle proteomics and stem cell biology, with an emphasis on studies that apply mass spectrometry to dissect pluripotency and differentiation.

Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging

We describe a mass spectrometry method, QuantMode, which improves the accuracy of isobaric tag–based quantification by alleviating the pervasive problem of precursor interference—co-isolation of impurities—through gas-phase purification. QuantMode analysis of a yeast sample ‘contaminated’ with interfering human peptides showed substantially improved quantitative accuracy compared to a standard scan, with a small loss of spectral identifications. This technique will allow large-scale, multiplexed quantitative proteomics analyses using isobaric tagging.

Higher-energy collision-activated dissociation without a dedicated collision cell

Beam-type collisional activation dissociation (HCD) offers many advantages over resonant excitation collision-activated dissociation, including improved identification of phosphorylated peptides and compatibility with isobaric tag-based quantitation (e.g. tandem mass tag (TMT) and iTRAQ). However, HCD typically requires specially designed and dedicated collision cells. Here we demonstrate that HCD can be performed in the ion injection pathway of a mass spectrometer with a standard atmospheric inlet (iHCD). Testing this method on complex peptide mixtures revealed similar identification rates to collision-activated dissociation (2883 versus 2730 IDs for iHCD/CAD, respectively) and precursor-product-conversion efficiency comparable to that achieved within a dedicated collision cell. Compared with pulsed-q dissociation, a quadrupole ion trap-based method that retains low-mass isobaric tag reporter ions, iHCD yielded isobaric tag for relative and absolute quantification reporter ions 10-fold more intense. This method involves no additional hardware and can theoretically be implemented on any mass spectrometer with an atmospheric inlet.

COMPASS: a suite of pre- and post-search proteomics software tools for OMSSA

Here we present the Coon OMSSA Proteomic Analysis Software Suite (COMPASS): a free and open-source software pipeline for high-throughput analysis of proteomics data, designed around the Open Mass Spectrometry Search Algorithm. We detail a synergistic set of tools for protein database generation, spectral reduction, peptide false discovery rate analysis, peptide quantitation via isobaric labeling, protein parsimony and protein false discovery rate analysis, and protein quantitation. We strive for maximum ease of use, utilizing graphical user interfaces and working with data files in the original instrument vendor format. Results are stored in plain text comma-separated value files, which are easy to view and manipulate with a text editor or spreadsheet program. We illustrate the operation and efficacy of COMPASS through the use of two LC-MS/MS data sets. The first is a data set of a highly annotated mixture of standard proteins and manually validated contaminants that exhibits the identification workflow. The second is a data set of yeast peptides, labeled with isobaric stable isotope tags and mixed in known ratios, to demonstrate the quantitative workflow. For these two data sets, COMPASS performs equivalently or better than the current de facto standard, the Trans-Proteomic Pipeline.