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.

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.

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.

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.

Role of transcription factor RUNX1 in the maintenance of T helper cell epigenome and rapid recall response

Memory T cells exhibit an accelerated response after a second encounter with the same antigen. In our previous studies, we showed that the ability to produce effector molecules within minutes of the secondary exposure to antigen (“rapid recall response”) is associated with the epigenetic state of the rapid recall genes. This memory epigenome is established during the primary T cell encounter with its cognate antigen. To maintain a “rapid recall response” even when antigen is eliminated, resting memory T cells would have to maintain open chromatin signature for years despite episodic homeostatic T cell proliferation. However, the mechanisms underlying the long-term maintenance of this epigenetic pattern remains unclear. Here, we used 10X Multiome (scRNA-seq and scATAC-seq obtained from the same cells) analysis to show that the association between the chromatin state of memory cells and their cytokine expression upon activation is Th lineage specific.

Bioinformatic analysis showed that open chromatin specific for memory but not naïve CD4 T cells is enriched with motifs of constitutively expressed transcription factors RUNX, SP1, and ETS. Our preliminary Cut&Tag data in total human resting CD4 T cells demonstrates that RUNX1 binds DNA close to T helper cytokines genes (IL3, IL4, IL9, IL10, IL13, IFNG) at “memory-specific” ATAC-seq regions, suggesting the role of RUNX1 in maintaining this “rapid recall” epigenome. Together with the dynamic model of transcriptome and epigenome rearrangements this data could help us to understand how memory T cells establish a platform that enables and maintains their unique ability to respond more rapidly to subsequent encounters with antigen.

Supported by R01AI153442-01A1 from NIAID, NIH

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-1β 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.

B-cell lymphoma 6 protein modulates function of human tonsillar innate lymphoid cells

Innate lymphoid cells (ILC) play a fundamental role in mucosal homeostasis and immunity via expression of cytokines such as IL-22, IL-17A and IFN-γ. However, the transcriptional network that controls ILC functional identity is incompletely defined. Previously, we demonstrated that BCL6 plays a key role in transcriptional regulation in mouse intestinal ILC1 and ILC3. Here, we performed in vitro cultures of ILCs isolated from human tonsil in the presence of cytokines that promote ILC1 or ILC3 in order to assess the role of BCL6 in functional plasticity of ILCs. ILCs were treated with IL-2 and IL-12/IL-1β or IL-23/IL-1β in the presence or absence of the BCL6 inhibitor FX-1. In the context of IL-12/IL-1β ILC1-promoting culture, FX-1 inhibition of BCL6 reduced expression of Tbet and IFN-γ. In contrast, BCL6 inhibition in the context of IL-23/IL-1β ILC3-inducing conditions had no effect on Tbet, IFN-γ, or RORγt. In either context, FX-1 resulted in reduced IL-22 but increased IL-17A expression in comparison to vehicle treated cultures. Thus, our data emphasizes the role of BCL6 as a regulator of human tonsil ILC functional identity.

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.

Role for B-cell lymphoma 6 in intestinal innate lymphoid cell homeostasis and inflammatory disease

Group 3 innate lymphoid cells (ILC3) are critical for intestinal homeostasis. The functional activity of ILC3 must be tightly regulated as cytokines (e.g. IL-17 and IL-22) produced by these cells are critical for antimicrobial immune defense but can also contribute to intestinal disease when produced in excess. This regulation is achieved via incompletely defined transcriptional networks that include B-cell lymphoma 6 (BCL6), which we recently demonstrated is critical for maintaining ILC1- and ILC3-specific gene expression programs during homeostasis (Pokrovskii et al 2019). We now show that Bcl6-deficient small intestine lamina propria ILC3 exhibit elevated expression of the transcription factors retinoic acid receptor-related orphan nuclear receptor alpha (RORα) and RORγt. These factors are known to support ILC3 cytokine expression, potentially explaining elevated Il17a and Il17f expression by Bcl6-deficient ILC3. Of note, expression of Il22ra2 (IL-22 binding) was also markedly increased in these ILC3, likely limiting bioavailability of IL-22. Accordingly, these mice exhibit marked reduction in IL-22-dependent expression of epithelium-associated anti-microbial peptides in the terminal ileum. Of note, Bcl6ΔILC mice exhibited augmented susceptibility to dextran sodium sulfate-induced inflammatory bowel disease. Thus, BCL6 expression in ILC3-intrinsic BCL6 expression is important for mucosal immune defense and limiting intestinal inflammation.

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.

Due to increasing availability of biological data, methods to properly integrate data generated across the globe become essential for extracting reproducible insights into relevant research questions. In this work, we developed a framework to reconstruct gene regulatory networks from expression datasets generated in separate studies—and thus, because of technical variation (different dates, handlers, laboratories, protocols etc…), challenging to integrate. Since regulatory mechanisms are often shared across conditions, we hypothesized that drawing conclusions from various data sources would improve performance of gene regulatory network inference. By transferring knowledge among regulatory models, our method is able to detect weaker patterns that are conserved across datasets, while also being able to detect dataset-unique interactions. We also allow incorporation of prior knowledge on network structure to favor models that are somewhat similar to the prior itself. Using two model organisms, we show that joint network inference outperforms inference from a single dataset. We also demonstrate that our method is robust to false edges in the prior and to low condition overlap across datasets, and that it can outperform current data integration strategies.

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.

Critical role of IRF1 and BATF in forming chromatin landscape during type 1 regulatory cell differentiation

Type 1 regulatory T cells (Tr1 cells) are induced by interleukin-27 (IL-27) and have critical roles in the control of autoimmunity and resolution of inflammation. We found that the transcription factors IRF1 and BATF were induced early on after treatment with IL-27 and were required for the differentiation and function of Tr1 cells in vitro and in vivo. Epigenetic and transcriptional analyses revealed that both transcription factors influenced chromatin accessibility and expression of the genes required for Tr1 cell function. IRF1 and BATF deficiencies uniquely altered the chromatin landscape, suggesting that these factors serve a pioneering function during Tr1 cell differentiation.

A Damage-Independent Role for 53BP1 that Impacts Break Order and Igh Architecture during Class Switch Recombination

During class switch recombination (CSR), B cells replace the Igh Cμ or δ exons with another down-stream constant region exon (C_H), altering the anti-body isotype. CSR occurs through the introduction of AID-mediated double-strand breaks (DSBs) in switch regions and subsequent ligation of broken ends. Here, we developed an assay to investigate the dynamics of DSB formation in individual cells. We demonstrate that the upstream switch region Sμ is first targeted during recombination and that the mechanism underlying this control relies on 53BP1. Surprisingly, regulation of break order occurs through residual binding of 53BP1 to chromatin before the introduction of damage and independent of its established role in DNA repair. Using chromosome conformation capture, we show that 53BP1 mediates changes in chromatin architecture that affect break order. Finally, our results explain how changes in Igh architecture in the absence of 53BP1 could promote inversional rearrangements that compromise CSR.

4C-ker: A Method to Reproducibly Identify Genome-Wide Interactions Captured by 4C-Seq Experiments

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

An IL-23R/IL-22 Circuit Regulates Epithelial Serum Amyloid A to Promote Local Effector Th17 Responses

RORγt(+) Th17 cells are important for mucosal defenses but also contribute to autoimmune disease. They accumulate in the intestine in response to microbiota and produce IL-17 cytokines. Segmented filamentous bacteria (SFB) are Th17-inducing commensals that potentiate autoimmunity in mice. RORγt(+) T cells were induced in mesenteric lymph nodes early after SFB colonization and distributed across different segments of the gastrointestinal tract. However, robust IL-17A production was restricted to the ileum, where SFB makes direct contact with the epithelium and induces serum amyloid A proteins 1 and 2 (SAA1/2), which promote local IL-17A expression in RORγt(+) T cells. We identified an SFB-dependent role of type 3 innate lymphoid cells (ILC3), which secreted IL-22 that induced epithelial SAA production in a Stat3-dependent manner. This highlights the critical role of tissue microenvironment in activating effector functions of committed Th17 cells, which may have important implications for how these cells contribute to inflammatory disease.

Sparse and compositionally robust inference of microbial ecological networks

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

CX₃CR1⁺ mononuclear phagocytes support colitis-associated innate lymphoid cell production of IL-22

Interleukin (IL)-22-producing group 3 innate lymphoid cells (ILC3) promote mucosal healing and maintain barrier integrity, but how microbial signals are integrated to regulate mucosal protection offered by these cells remains unclear. Here, we show that in vivo depletion of CX₃CR1⁺ mononuclear phagocytes (MNPs) resulted in more severe colitis and death after infection with Citrobacter rodentium. This phenotype was rescued by exogenous IL-22, which was endogenously produced by ILC3 in close spatial proximity to CX₃CR1⁺ MNPs that were dependent on MyD88 signaling. CX₃CR1⁺MNPs from both mouse and human tissue produced more IL-23 and IL-1β than conventional CD103(+) dendritic cells (cDCs) and were more efficient than cDCs in supporting IL-22 production in ILC3 in vitro and in vivo. Further, colonic ILC3 from patients with mild to moderate ulcerative colitis or Crohn's disease had increased IL-22 production. IBD-associated SNP gene set analysis revealed enrichment for genes selectively expressed in human intestinal MNPs. The product of one of these, TL1A, potently enhanced IL-23- and IL-1β-induced production of IL-22 and GM-CSF by ILC3. Collectively, these results reveal a critical role for CX₃CR1⁺ mononuclear phagocytes in integrating microbial signals to regulate colonic ILC3 function in IBD.

Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

Phosphotyrosine signaling analysis of site-specific mutations on EGFRvIII identifies determinants governing glioblastoma cell growth

To evaluate the role of individual EGFR phosphorylation sites in activating components of the cellular signaling network we have performed a mass spectrometry-based analysis of the phosphotyrosine network downstream of site-specific EGFRvIII mutants, enabling quantification of network-level effects of site-specific point mutations. Mutation at Y845, Y1068 or Y1148 resulted in diminished receptor phosphorylation, while mutation at Y1173 led to increased phosphorylation on multiple EGFRvIII residues. Altered phosphorylation at the receptor was recapitulated in downstream signaling network activation levels, with Y1173F mutation leading to increased phosphorylation throughout the network. Computational modeling of GBM cell growth as a function of network phosphorylation levels highlights the Erk pathway as crucial for regulating EGFRvIII-driven U87MG GBM cell behavior, with the unexpected finding that Erk1/2 is negatively correlated to GBM cell growth. Genetic manipulation of this pathway supports the model, demonstrating that EGFRvIII-expressing U87MG GBM cells are sensitive to Erk activation levels. Additionally, we developed a model describing glioblastoma cell growth based on a reduced set of phosphoproteins, which represent potential candidates for future development as therapeutic targets for EGFRvIII-positive glioblastoma patients.