Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk.

Inflammatory Bowel Disease ( IBD ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response. Genome-wide association studies ( GWAS ) have identified 418 independent genetic risk loci contributing to IBD, yet the majority are noncoding and their mechanisms of action are difficult to decipher. If causal, they likely alter transcription factor ( TF ) binding and downstream gene expression in particular cell types and contexts. To bridge this knowledge gap, we built a novel resource: multiome-seq (tandem single-nuclei ( sn )RNA-seq and chromatin accessibility ( snATAC )-seq) of intestinal tissue from pediatric IBD patients, where anti-TNF response was defined by endoscopic healing. From the snATAC-seq data, we generated a first-time atlas of chromatin accessibility (putative regulatory elements) for diverse intestinal cell types in the context of IBD. For cell types/contexts mediating genetic risk, we reasoned that accessible chromatin will co-localize with genetic disease risk loci. We systematically tested for significant co-localization of our chromatin accessibility maps and risk variants for 758 GWAS traits. Globally, genetic risk variants for IBD, autoimmune and inflammatory diseases are enriched in accessible chromatin of immune populations, while other traits (e.g., colorectal cancer, metabolic) are enriched in epithelial and stromal populations. This resource opens new avenues to uncover the complex molecular and cellular mechanisms mediating genetic disease risk.

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy.

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

Alveolar epithelial progenitor cells require Nkx2-1 to maintain progenitor-specific epigenomic state during lung homeostasis and regeneration.

Lung epithelial regeneration after acute injury requires coordination cellular coordination to pattern the morphologically complex alveolar gas exchange surface. During adult lung regeneration, Wnt-responsive alveolar epithelial progenitor (AEP) cells, a subset of alveolar type 2 (AT2) cells, proliferate and transition to alveolar type 1 (AT1) cells. Here, we report a refined primary murine alveolar organoid, which recapitulates critical aspects of in vivo regeneration. Paired scRNAseq and scATACseq followed by transcriptional regulatory network (TRN) analysis identified two AT1 transition states driven by distinct regulatory networks controlled in part by differential activity of Nkx2-1. Genetic ablation of Nkx2-1 in AEP-derived organoids was sufficient to cause transition to a proliferative stressed Krt8+ state, and AEP-specific deletion of Nkx2-1 in adult mice led to rapid loss of progenitor state and uncontrolled growth of Krt8+ cells. Together, these data implicate dynamic epigenetic maintenance via Nkx2-1 as central to the control of facultative progenitor activity in AEPs.

Accumulation of immune-suppressive CD4 + T cells in aging - tempering inflammaging at the expense of immunity.

The 'immune risk profile' has been shown to predict mortality in the elderly, highlighting the need to better understand age-related immune dysfunction. While aging leads to many defects affecting all arms of the immune system, this review is focused on the accrual of immuno-suppressive CD4 + T cell populations, including FoxP3 + regulatory T cells, and subsets of IL-10-producing T follicular helper cells. New data suggest that such accumulations constitute feedback mechanisms to temper the ongoing progressive low-grade inflammation that develops with age, the so-called "inflammaging", and by doing so, how they have the potential to promote healthier aging. However, they also impair effector immune responses, notably to infections, or vaccines. These studies also reinforce the idea that the aged immune system should not be considered as a poorly functional version of the young one, but more as a dynamic system in which CD4 + T cells, and other immune/non-immune subsets, differentiate, interact with their milieu and function differently than in young hosts. A better understanding of these unique interactions is thus needed to improve effector immune responses in the elderly, while keeping inflammaging under control.

An atlas of gene regulatory networks for memory CD4 + T cells in youth and old age.

Aging profoundly affects immune-system function, promoting susceptibility to pathogens, cancers and chronic inflammation. We previously identified a population of IL-10-producing, T follicular helper-like cells (" Tfh10 "), linked to suppressed vaccine responses in aged mice. Here, we integrate single-cell ( sc )RNA-seq, scATAC-seq and genome-scale modeling to characterize Tfh10 - and the full CD4 + memory T cell ( CD4 + TM ) compartment - in young and old mice. We identified 13 CD4 + TM populations, which we validated through cross-comparison to prior scRNA-seq studies. We built gene regulatory networks ( GRNs ) that predict transcription-factor control of gene expression in each T-cell population and how these circuits change with age. Through integration with pan-cell aging atlases, we identified intercellular-signaling networks driving age-dependent changes in CD4 + TM. Our atlas of finely resolved CD4 + TM subsets, GRNs and cell-cell communication networks is a comprehensive resource of predicted regulatory mechanisms operative in memory T cells, presenting new opportunities to improve immune responses in the elderly.

Elevated CD153 Expression on Aged T Follicular Helper Cells is Vital for B cell Responses.

Our recent data showed that an aberrant IL-10-producing T follicular helper population (Tfh10) accumulates dramatically with age and is associated with age-related declines in vaccine responsiveness. Through single cell gene expression and chromatin accessibility analysis of IL-10+ and IL-10- memory CD4+ T cells from young and aged mice, we identified increased expression of CD153 on aged Tfh and Tfh10 cells. Mechanistically, we linked inflammaging (increased IL-6 levels) to elevated CD153 expression of Tfh cells through c-Maf. Surprisingly, blockade of CD153 in aged mice significantly reduced their vaccine-driven antibody response, which was associated with decreased expression of ICOS on antigen-specific Tfh cells. Combined, these data show that an IL-6/c-Maf/CD153 circuit is critical for maintaining ICOS expression. Thus, although overall Tfh-mediated B cell responses are reduced in the context of vaccines and aging, our data suggest that elevated expression of CD153 on Tfh cells potentiates the remaining Tfh function in aged mice.

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.

Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates.

Perinatal inflammatory stress is associated with early life morbidity and lifelong consequences for pulmonary health. Chorioamnionitis, an inflammatory condition affecting the placenta and fluid surrounding the developing fetus, affects 25 to 40% of preterm births. Severe chorioamnionitis with preterm birth is associated with significantly increased risk of pulmonary disease and secondary infections in childhood, suggesting that fetal inflammation may markedly alter the development of the lung. Here, we used intra-amniotic lipopolysaccharide (LPS) challenge to induce experimental chorioamnionitis in a prenatal rhesus macaque (Macaca mulatta) model that mirrors structural and temporal aspects of human lung development. Inflammatory injury directly disrupted the developing gas exchange surface of the primate lung, with extensive damage to alveolar structure, particularly the close association and coordinated differentiation of alveolar type 1 pneumocytes and specialized alveolar capillary endothelium. Single-cell RNA sequencing analysis defined a multicellular alveolar signaling niche driving alveologenesis that was extensively disrupted by perinatal inflammation, leading to a loss of gas exchange surface and alveolar simplification, with notable resemblance to chronic lung disease in newborns. Blockade of the inflammatory cytokines interleukin-1ß and tumor necrosis factor-α ameliorated LPS-induced inflammatory lung injury by blunting stromal responses to inflammation and modulating innate immune activation in myeloid cells, restoring structural integrity and key signaling networks in the developing alveolus. These data provide new insight into the pathophysiology of developmental lung injury and suggest that modulating inflammation is a promising therapeutic approach to prevent fetal consequences of chorioamnionitis.

High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.

MOTIVATION: Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. RESULTS: In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. AVAILABILITY AND IMPLEMENTATION: The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TOP1 inhibition therapy protects against SARS-CoV-2-induced lethal inflammation.

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently affecting millions of lives worldwide. Large retrospective studies indicate that an elevated level of inflammatory cytokines and pro-inflammatory factors are associated with both increased disease severity and mortality. Here, using multidimensional epigenetic, transcriptional, in vitro, and in vivo analyses, we report that topoisomerase 1 (TOP1) inhibition suppresses lethal inflammation induced by SARS-CoV-2. Therapeutic treatment with two doses of topotecan (TPT), an FDA-approved TOP1 inhibitor, suppresses infection-induced inflammation in hamsters. TPT treatment as late as 4 days post-infection reduces morbidity and rescues mortality in a transgenic mouse model. These results support the potential of TOP1 inhibition as an effective host-directed therapy against severe SARS-CoV-2 infection. TPT and its derivatives are inexpensive clinical-grade inhibitors available in most countries. Clinical trials are needed to evaluate the efficacy of repurposing TOP1 inhibitors for severe coronavirus disease 2019 (COVID-19) in humans.

Global discovery of lupus genetic risk variant allelic enhancer activity.

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we construct a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 reveals 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

Topoisomerase 1 inhibition therapy protects against SARS-CoV-2-induced inflammation and death in animal models.

The ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is currently affecting millions of lives worldwide. Large retrospective studies indicate that an elevated level of inflammatory cytokines and pro-inflammatory factors are associated with both increased disease severity and mortality. Here, using multidimensional epigenetic, transcriptional, in vitro and in vivo analyses, we report that Topoisomerase 1 (Top1) inhibition suppresses lethal inflammation induced by SARS-CoV-2. Therapeutic treatment with two doses of Topotecan (TPT), a FDA-approved Top1 inhibitor, suppresses infection-induced inflammation in hamsters. TPT treatment as late as four days post-infection reduces morbidity and rescues mortality in a transgenic mouse model. These results support the potential of Top1 inhibition as an effective host-directed therapy against severe SARS-CoV-2 infection. TPT and its derivatives are inexpensive clinical-grade inhibitors available in most countries. Clinical trials are needed to evaluate the efficacy of repurposing Top1 inhibitors for COVID-19 in humans.

Not Chopped Liver-A Careful, Fate-Mapping Study of Macrophages in NASH.

Macrophages are central to the pathogenesis of non-alcoholic steatohepatitis (NASH). However, the identities and functional relationships between tissue-resident and tissue-recruited macrophages in NASH remain poorly understood. A recent study from Seidman et al. (2020) elucidates, at single-cell resolution, the fates, niches, and regulatory landscapes of liver tissue-resident and tissue-recruited macrophage populations in NASH.

Alternative Activation of Macrophages Is Accompanied by Chromatin Remodeling Associated with Lineage-Dependent DNA Shape Features Flanking PU.1 Motifs.

IL-4 activates macrophages to adopt distinct phenotypes associated with clearance of helminth infections and tissue repair, but the phenotype depends on the cellular lineage of these macrophages. The molecular basis of chromatin remodeling in response to IL-4 stimulation in tissue-resident and monocyte-derived macrophages is not understood. In this study, we find that IL-4 activation of different lineages of peritoneal macrophages in mice is accompanied by lineage-specific chromatin remodeling in regions enriched with binding motifs of the pioneer transcription factor PU.1. PU.1 motif is similarly associated with both tissue-resident and monocyte-derived IL-4-induced accessible regions but has different lineage-specific DNA shape features and predicted cofactors. Mutation studies based on natural genetic variation between C57BL/6 and BALB/c mouse strains indicate that accessibility of these IL-4-induced regions can be regulated through differences in DNA shape without direct disruption of PU.1 motifs. We propose a model whereby DNA shape features of stimulation-dependent genomic elements contribute to differences in the accessible chromatin landscape of alternatively activated macrophages on different genetic backgrounds that may contribute to phenotypic variations in immune responses.

Hybrid Gene Origination Creates Human-Virus Chimeric Proteins during Infection.

RNA viruses are a major human health threat. The life cycles of many highly pathogenic RNA viruses like influenza A virus (IAV) and Lassa virus depends on host mRNA, because viral polymerases cleave 5'-m7G-capped host transcripts to prime viral mRNA synthesis ("cap-snatching"). We hypothesized that start codons within cap-snatched host transcripts could generate chimeric human-viral mRNAs with coding potential. We report the existence of this mechanism of gene origination, which we named "start-snatching." Depending on the reading frame, start-snatching allows the translation of host and viral "untranslated regions" (UTRs) to create N-terminally extended viral proteins or entirely novel polypeptides by genetic overprinting. We show that both types of chimeric proteins are made in IAV-infected cells, generate T cell responses, and contribute to virulence. Our results indicate that during infection with IAV, and likely a multitude of other human, animal and plant viruses, a host-dependent mechanism allows the genesis of hybrid genes.

Characterization of Transcriptional Regulatory Networks that Promote and Restrict Identities and Functions of Intestinal Innate Lymphoid Cells.

Innate lymphoid cells (ILCs) promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. ILCs exhibit phenotypic and functional plasticity in response to environmental stimuli, yet the transcriptional regulatory networks (TRNs) that control ILC function are largely unknown. Here, we integrate gene expression and chromatin accessibility data to infer regulatory interactions between transcription factors (TFs) and genes within intestinal type 1, 2, and 3 ILC subsets. We predicted the "core" TFs driving ILC identities, organized TFs into cooperative modules controlling distinct gene programs, and validated roles for c-MAF and BCL6 as regulators affecting type 1 and type 3 ILC lineages. The ILC network revealed alternative-lineage-gene repression, a mechanism that may contribute to reported plasticity between ILC subsets. By connecting TFs to genes, the TRNs suggest means to selectively regulate ILC effector functions, while our network approach is broadly applicable to identifying regulators in other in vivo cell populations.

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.

Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)-seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs ("TF-TF modules") in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

Multi-study inference of regulatory networks for more accurate models of gene regulation.

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Analysis of 3D genomic interactions identifies candidate host genes that transposable elements potentially regulate.

BACKGROUND: The organization of chromatin in the nucleus plays an essential role in gene regulation. About half of the mammalian genome comprises transposable elements. Given their repetitive nature, reads associated with these elements are generally discarded or randomly distributed among elements of the same type in genome-wide analyses. Thus, it is challenging to identify the activities and properties of individual transposons. As a result, we only have a partial understanding of how transposons contribute to chromatin folding and how they impact gene regulation. RESULTS: Using PCR and Capture-based chromosome conformation capture (3C) approaches, collectively called 4Tran, we take advantage of the repetitive nature of transposons to capture interactions from multiple copies of endogenous retrovirus (ERVs) in the human and mouse genomes. With 4Tran-PCR, reads are selectively mapped to unique regions in the genome. This enables the identification of transposable element interaction profiles for individual ERV families and integration events specific to particular genomes. With this approach, we demonstrate that transposons engage in long-range intra-chromosomal interactions guided by the separation of chromosomes into A and B compartments as well as topologically associated domains (TADs). In contrast to 4Tran-PCR, Capture-4Tran can uniquely identify both ends of an interaction that involve retroviral repeat sequences, providing a powerful tool for uncovering the individual transposable element insertions that interact with and potentially regulate target genes. CONCLUSIONS: 4Tran provides new insight into the manner in which transposons contribute to chromosome architecture and identifies target genes that transposable elements can potentially control.