Loop Catalog: a comprehensive HiChIP database of human and mouse samples.

HiChIP enables cost-effective and high-resolution profiling of regulatory and structural loops. To leverage the increasing number of publicly available HiChIP datasets from diverse cell lines and primary cells, we developed the Loop Catalog (https://loopcatalog.lji.org), a web-based database featuring HiChIP loop calls for 1319 samples across 133 studies and 44 high-resolution Hi-C loop calls. We demonstrate its utility in interpreting fine-mapped GWAS variants (SNP-to-gene linking), in identifying enriched sequence motifs and motif pairs at loop anchors, and in network-level analysis of loops connecting regulatory elements (community detection). Our comprehensive catalog, spanning over 4M unique 5kb loops, along with the accompanying analysis modalities constitutes an important resource for studies in gene regulation and genome organization.

Lineage-specific 3D genome organization is assembled at multiple scales by IKAROS.

A generic level of chromatin organization generated by the interplay between cohesin and CTCF suffices to limit promiscuous interactions between regulatory elements, but a lineage-specific chromatin assembly that supersedes these constraints is required to configure the genome to guide gene expression changes that drive faithful lineage progression. Loss-of-function approaches in B cell precursors show that IKAROS assembles interactions across megabase distances in preparation for lymphoid development. Interactions emanating from IKAROS-bound enhancers override CTCF-imposed boundaries to assemble lineage-specific regulatory units built on a backbone of smaller invariant topological domains. Gain of function in epithelial cells confirms IKAROS' ability to reconfigure chromatin architecture at multiple scales. Although the compaction of the Igκ locus required for genome editing represents a function of IKAROS unique to lymphocytes, the more general function to preconfigure the genome to support lineage-specific gene expression and suppress activation of extra-lineage genes provides a paradigm for lineage restriction.

Single-cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants.

The impact of genetic variants on cells challenged in biologically relevant contexts has not been fully explored. Here, we activated CD4+ T cells from 89 healthy donors and performed a single-cell RNA sequencing assay with >1 million cells to examine cell type-specific and activation-dependent effects of genetic variants. Single-cell expression quantitative trait loci (sc-eQTL) analysis of 19 distinct CD4+ T cell subsets showed that the expression of over 4000 genes is significantly associated with common genetic polymorphisms and that most of these genes show their most prominent effects in specific cell types. These genes included many that encode for molecules important for activation, differentiation, and effector functions of T cells. We also found new gene associations for disease-risk variants identified from genome-wide association studies and highlighted the cell types in which their effects are most prominent. We found that biological sex has a major influence on activation-dependent gene expression in CD4+ T cell subsets. Sex-biased transcripts were significantly enriched in several pathways that are essential for the initiation and execution of effector functions by CD4+ T cells like TCR signaling, cytokines, cytokine receptors, costimulatory, apoptosis, and cell-cell adhesion pathways. Overall, this DICE (Database of Immune Cell Expression, eQTLs, and Epigenomics) subproject highlights the power of sc-eQTL studies for simultaneously exploring the activation and cell type-dependent effects of common genetic variants on gene expression (https://dice-database.org).

COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types.

Common genetic polymorphisms associated with COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Given the importance of immune cells in the pathogenesis of COVID-19 illness, here we assessed the effects of COVID-19-risk variants on gene expression in a wide range of immune cell types. Transcriptome-wide association study and colocalization analysis revealed putative causal genes and the specific immune cell types where gene expression is most influenced by COVID-19-risk variants. Notable examples include OAS1 in non-classical monocytes, DTX1 in B cells, IL10RB in NK cells, CXCR6 in follicular helper T cells, CCR9 in regulatory T cells and ARL17A in TH2 cells. By analysis of transposase accessible chromatin and H3K27ac-based chromatin-interaction maps of immune cell types, we prioritized potentially functional COVID-19-risk variants. Our study highlights the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.

Promoter-interacting expression quantitative trait loci are enriched for functional genetic variants.

Expression quantitative trait loci (eQTLs) studies provide associations of genetic variants with gene expression but fall short of pinpointing functionally important eQTLs. Here, using H3K27ac HiChIP assays, we mapped eQTLs overlapping active cis-regulatory elements that interact with their target gene promoters (promoter-interacting eQTLs, pieQTLs) in five common immune cell types (Database of Immune Cell Expression, Expression quantitative trait loci and Epigenomics (DICE) cis-interactome project). This approach allowed us to identify functionally important eQTLs and show mechanisms that explain their cell-type restriction. We also devised an approach to eQTL discovery that relies on HiChIP-based promoter interaction maps as a structural framework for deciding which SNPs to test for association with gene expression, and observe ultra-long-distance pieQTLs (>1 megabase away), including several disease-risk variants. We validated the functional role of pieQTLs using reporter assays, CRISPRi, dCas9-tiling guides and Cas9-mediated base-pair editing. In this article we present a method for functional eQTL discovery and provide insights into relevance of noncoding variants for cell-specific gene regulation and for disease association beyond conventional eQTL mapping.

COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types.

Common genetic polymorphisms associated with severity of COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Here, we assessed the effects of 679 COVID-19-risk variants on gene expression in a wide-range of immune cell types. Severe COVID-19-risk variants were significantly associated with the expression of 11 protein-coding genes, and overlapped with either target gene promoter or cis -regulatory regions that interact with target promoters in the cell types where their effects are most prominent. For example, we identified that the association between variants in the 3p21.31 risk locus and the expression of CCR2 in classical monocytes is likely mediated through an active cis-regulatory region that interacted with CCR2 promoter specifically in monocytes. The expression of several other genes showed prominent genotype-dependent effects in non-classical monocytes, NK cells, B cells, or specific T cell subtypes, highlighting the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.

Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2.

Fit-Hi-C is a programming application to compute statistical confidence estimates for Hi-C contact maps to identify significant chromatin contacts. By fitting a monotonically non-increasing spline, Fit-Hi-C captures the relationship between genomic distance and contact probability without any parametric assumption. The spline fit together with the correction of contact probabilities with respect to bin- or locus-specific biases accounts for previously characterized covariates impacting Hi-C contact counts. Fit-Hi-C is best applied for the study of mid-range (e.g., 20 kb-2 Mb for human genome) intra-chromosomal contacts; however, with the latest reimplementation, named FitHiC2, it is possible to perform genome-wide analysis for high-resolution Hi-C data, including all intra-chromosomal distances and inter-chromosomal contacts. FitHiC2 also offers a merging filter module, which eliminates indirect/bystander interactions, leading to significant reduction in the number of reported contacts without sacrificing recovery of key loops such as those between convergent CTCF binding sites. Here, we describe how to apply the FitHiC2 protocol to three use cases: (i) 5-kb resolution Hi-C data of chromosome 5 from GM12878 (a human lymphoblastoid cell line), (ii) 40-kb resolution whole-genome Hi-C data from IMR90 (human lung fibroblast), and (iii) budding yeast whole-genome Hi-C data at a single restriction cut site (EcoRI) resolution. The procedure takes ~12 h with preprocessing when all use cases are run sequentially (~4 h when run parallel). With the recent improvements in its implementation, FitHiC2 (8 processors and 16 GB memory) is also scalable to genome-wide analysis of the highest resolution (1 kb) Hi-C data available to date (~48 h with 32 GB peak memory). FitHiC2 is available through Bioconda, GitHub and the Python Package Index.

NSD2 overexpression drives clustered chromatin and transcriptional changes in a subset of insulated domains.

CTCF and cohesin play a key role in organizing chromatin into topologically associating domain (TAD) structures. Disruption of a single CTCF binding site is sufficient to change chromosomal interactions leading to alterations in chromatin modifications and gene regulation. However, the extent to which alterations in chromatin modifications can disrupt 3D chromosome organization leading to transcriptional changes is unknown. In multiple myeloma, a 4;14 translocation induces overexpression of the histone methyltransferase, NSD2, resulting in expansion of H3K36me2 and shrinkage of antagonistic H3K27me3 domains. Using isogenic cell lines producing high and low levels of NSD2, here we find oncogene activation is linked to alterations in H3K27ac and CTCF within H3K36me2 enriched chromatin. A logistic regression model reveals that differentially expressed genes are significantly enriched within the same insulated domain as altered H3K27ac and CTCF peaks. These results identify a bidirectional relationship between 2D chromatin and 3D genome organization in gene regulation.

Identification of significant chromatin contacts from HiChIP data by FitHiChIP.

HiChIP/PLAC-seq is increasingly becoming popular for profiling 3D chromatin contacts among regulatory elements and for annotating functions of genetic variants. Here we describe FitHiChIP, a computational method for loop calling from HiChIP/PLAC-seq data, which jointly models the non-uniform coverage and genomic distance scaling of contact counts to compute statistical significance estimates. We also develop a technique to filter putative bystander loops that can be explained by stronger adjacent loops. Compared to existing methods, FitHiChIP performs better in recovering contacts reported by Hi-C, promoter capture Hi-C and ChIA-PET experiments and in capturing previously validated promoter-enhancer interactions. FitHiChIP loop calls are reproducible among replicates and are consistent across different experimental settings. Our work also provides a framework for differential HiChIP analysis with an option to utilize ChIP-seq data for further characterizing differential loops. Even though designed for HiChIP, FitHiChIP is also applicable to other conformation capture assays.

Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer.

High numbers of tissue-resident memory T (TRM) cells are associated with better clinical outcomes in cancer patients. However, the molecular characteristics that drive their efficient immune response to tumors are poorly understood. Here, single-cell and bulk transcriptomic analysis of TRM and non-TRM cells present in tumor and normal lung tissue from patients with lung cancer revealed that PD-1-expressing TRM cells in tumors were clonally expanded and enriched for transcripts linked to cell proliferation and cytotoxicity when compared with PD-1-expressing non-TRM cells. This feature was more prominent in the TRM cell subset coexpressing PD-1 and TIM-3, and it was validated by functional assays ex vivo and also reflected in their chromatin accessibility profile. This PD-1+TIM-3+ TRM cell subset was enriched in responders to PD-1 inhibitors and in tumors with a greater magnitude of CTL responses. These data highlight that not all CTLs expressing PD-1 are dysfunctional; on the contrary, TRM cells with PD-1 expression were enriched for features suggestive of superior functionality.

Reduced expression of phosphatase PTPN2 promotes pathogenic conversion of Tregs in autoimmunity.

Genetic variants at the PTPN2 locus, which encodes the tyrosine phosphatase PTPN2, cause reduced gene expression and are linked to rheumatoid arthritis (RA) and other autoimmune diseases. PTPN2 inhibits signaling through the T cell and cytokine receptors, and loss of PTPN2 promotes T cell expansion and CD4- and CD8-driven autoimmunity. However, it remains unknown whether loss of PTPN2 in FoxP3+ regulatory T cells (Tregs) plays a role in autoimmunity. Here we aimed to model human autoimmune-predisposing PTPN2 variants, the presence of which results in a partial loss of PTPN2 expression, in mouse models of RA. We identified that reduced expression of Ptpn2 enhanced the severity of autoimmune arthritis in the T cell-dependent SKG mouse model and demonstrated that this phenotype was mediated through a Treg-intrinsic mechanism. Mechanistically, we found that through dephosphorylation of STAT3, PTPN2 inhibits IL-6-driven pathogenic loss of FoxP3 after Tregs have acquired RORγt expression, at a stage when chromatin accessibility for STAT3-targeted IL-17-associated transcription factors is maximized. We conclude that PTPN2 promotes FoxP3 stability in mouse RORγt+ Tregs and that loss of function of PTPN2 in Tregs contributes to the association between PTPN2 and autoimmunity.

IDXL: Species Tree Inference Using Internode Distance and Excess Gene Leaf Count.

We propose an extension of the distance matrix methods NJst and ASTRID to infer species trees from incongruent gene trees having Incomplete Lineage Sorting. Both approaches consider the average internode distance (ID) between individual taxa pairs as the distance measure. The measure ID does not use the root of a tree, and thus may not always infer the relative position of a taxon with respect to the root. We define a novel distance measure excess gene leaf count (XL) between individual couplets. The XL measure is computed using the root of a tree. It is proved to be additive, and is shown to infer the relative order of divergence among individual couplets better. We propose a novel method IDXL which uses both the XL and ID measures for species tree construction. IDXL is shown to perform better than NJst and other distance matrix approaches for most of the biological and simulated datasets. Having the same computational complexity as NJst, IDXL can be applied for species tree inference on large-scale biological datasets.

COSPEDTree: COuplet Supertree by Equivalence Partitioning of Taxa Set and DAG Formation.

From a set of phylogenetic trees with overlapping taxa set, a supertree exhibits evolutionary relationships among all input taxa. The key is to resolve the contradictory relationships with respect to input trees, between individual taxa subsets. Formulation of this NP hard problem employs either local search heuristics to reduce tree search space, or resolves the conflicts with respect to fixed or varying size subtree level decompositions. Different approximation techniques produce supertrees with considerable performance variations. Moreover, the majority of the algorithms involve high computational complexity, thus not suitable for use on large biological data sets. Current study presents COSPEDTree, a novel method for supertree construction. The technique resolves source tree conflicts by analyzing couplet (taxa pair) relationships for each source trees. Subsequently, individual taxa pairs are resolved with a single relation. To prioritize the consensus relations among individual taxa pairs for resolving them, greedy scoring is employed to assign higher score values for the consensus relations among a taxa pair. Selected set of relations resolving individual taxa pairs is subsequently used to construct a directed acyclic graph (DAG). Vertices of DAG represents a taxa subset inferred from the same speciation event. Thus, COSPEDTree can generate non-binary supertrees as well. Depth first traversal on this DAG yields final supertree. According to the performance metrics on branch dissimilarities (such as FP, FN and RF), COSPEDTree produces mostly conservative, well resolved supertrees. Specifically, RF metrics are mostly lower compared to the reference approaches, and FP values are lower apart from only strictly conservative (or veto) approaches. COSPEDTree has worst case time and space complexities of cubic and quadratic order, respectively, better or comparable to the reference approaches. Such high performance and low computational costs enable COSPEDTree to be applied on large scale biological data sets.

Detection of artifacts from high energy bursts in neonatal EEG.

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.