Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping.

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.

MMCT-Loop: a mix model-based pipeline for calling targeted 3D chromatin loops.

Protein-specific Chromatin Conformation Capture (3C)-based technologies have become essential for identifying distal genomic interactions with critical roles in gene regulation. The standard techniques include Chromatin Interaction Analysis by Paired-End Tag (ChIA-PET), in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) also known as PLAC-seq. To identify chromatin interactions from these data, a variety of computational methods have emerged. Although these state-of-art methods address many issues with loop calling, only few methods can fit different data types simultaneously, and the accuracy as well as the efficiency these approaches remains limited. Here we have generated a pipeline, MMCT-Loop, which ensures the accurate identification of strong loops as well as dynamic or weak loops through a mixed model. MMCT-Loop outperforms existing methods in accuracy, and the detected loops show higher activation functionality. To highlight the utility of MMCT-Loop, we applied it to conformational data derived from neural stem cell (NSCs) and uncovered several previously unidentified regulatory regions for key master regulators of stem cell identity. MMCT-Loop is an accurate and efficient loop caller for targeted conformation capture data, which supports raw data or pre-processed valid pairs as input, the output interactions are formatted and easily uploaded to a genome browser for visualization.

Loss of the Atrial Fibrillation-Related Gene, Zfhx3, Results in Atrial Dilation and Arrhythmias.

BACKGROUND: ZFHX3 (zinc finger homeobox 3), a gene that encodes a large transcription factor, is at the second-most significantly associated locus with atrial fibrillation (AF), but its function in the heart is unknown. This study aims to identify causative genetic variation related to AF at the ZFHX3 locus and examine the impact of Zfhx3 loss on cardiac function in mice. METHODS: CRISPR-Cas9 genome editing, chromatin immunoprecipitation, and luciferase assays in pluripotent stem cell-derived cardiomyocytes were used to identify causative genetic variation related to AF at the ZFHX3 locus. Cardiac function was assessed by echocardiography, magnetic resonance imaging, electrophysiology studies, calcium imaging, and RNA sequencing in mice with heterozygous and homozygous cardiomyocyte-restricted Zfhx3 loss (Zfhx3 Het and knockout, respectively). Human cardiac single-nucleus ATAC (assay for transposase-accessible chromatin)-sequencing data was analyzed to determine which genes in atrial cardiomyocytes are directly regulated by ZFHX3. RESULTS: We found single-nucleotide polymorphism (SNP) rs12931021 modulates an enhancer regulating ZFHX3 expression, and the AF risk allele is associated with decreased ZFHX3 transcription. We observed a gene-dose response in AF susceptibility with Zfhx3 knockout mice having higher incidence, frequency, and burden of AF than Zfhx3 Het and wild-type mice, with alterations in conduction velocity, atrial action potential duration, calcium handling and the development of atrial enlargement and thrombus, and dilated cardiomyopathy. Zfhx3 loss results in atrial-specific differential effects on genes and signaling pathways involved in cardiac pathophysiology and AF. CONCLUSIONS: Our findings implicate ZFHX3 as the causative gene at the 16q22 locus for AF, and cardiac abnormalities caused by loss of cardiac Zfhx3 are due to atrial-specific dysregulation of pathways involved in AF susceptibility. Together, these data reveal a novel and important role for Zfhx3 in the control of cardiac genes and signaling pathways essential for normal atrial function.

Transfer learning enables predictions in network biology.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Genetics of myocardial interstitial fibrosis in the human heart and association with disease.

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor ß1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.

Single-nucleus RNA sequencing in ischemic cardiomyopathy reveals common transcriptional profile underlying end-stage heart failure.

Ischemic cardiomyopathy (ICM) is the leading cause of heart failure worldwide, yet the cellular and molecular signature of this disease is largely unclear. Using single-nucleus RNA sequencing (snRNA-seq) and integrated computational analyses, we profile the transcriptomes of over 99,000 human cardiac nuclei from the non-infarct region of the left ventricle of 7 ICM transplant recipients and 8 non-failing (NF) controls. We find the cellular composition of the ischemic heart is significantly altered, with decreased cardiomyocytes and increased proportions of lymphatic, angiogenic, and arterial endothelial cells in patients with ICM. We show that there is increased LAMININ signaling from endothelial cells to other cell types in ICM compared with NF. Finally, we find that the transcriptional changes that occur in ICM are similar to those in hypertrophic and dilated cardiomyopathies and that the mining of these combined datasets can identify druggable genes that could be used to target end-stage heart failure.

Integrated multi-omic characterization of congenital heart disease.

The heart, the first organ to develop in the embryo, undergoes complex morphogenesis that when defective results in congenital heart disease (CHD). With current therapies, more than 90% of patients with CHD survive into adulthood, but many suffer premature death from heart failure and non-cardiac causes1. Here, to gain insight into this disease progression, we performed single-nucleus RNA sequencing on 157,273 nuclei from control hearts and hearts from patients with CHD, including those with hypoplastic left heart syndrome (HLHS) and tetralogy of Fallot, two common forms of cyanotic CHD lesions, as well as dilated and hypertrophic cardiomyopathies. We observed CHD-specific cell states in cardiomyocytes, which showed evidence of insulin resistance and increased expression of genes associated with FOXO signalling and CRIM1. Cardiac fibroblasts in HLHS were enriched in a low-Hippo and high-YAP cell state characteristic of activated cardiac fibroblasts. Imaging mass cytometry uncovered a spatially resolved perivascular microenvironment consistent with an immunodeficient state in CHD. Peripheral immune cell profiling suggested deficient monocytic immunity in CHD, in agreement with the predilection in CHD to infection and cancer2. Our comprehensive phenotyping of CHD provides a roadmap towards future personalized treatments for CHD.

Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy.

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function1,2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFß1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

Mutations in Hcfc1 and Ronin result in an inborn error of cobalamin metabolism and ribosomopathy.

Combined methylmalonic acidemia and homocystinuria (cblC) is the most common inborn error of intracellular cobalamin metabolism and due to mutations in Methylmalonic Aciduria type C and Homocystinuria (MMACHC). Recently, mutations in the transcriptional regulators HCFC1 and RONIN (THAP11) were shown to result in cellular phenocopies of cblC. Since HCFC1/RONIN jointly regulate MMACHC, patients with mutations in these factors suffer from reduced MMACHC expression and exhibit a cblC-like disease. However, additional de-regulated genes and the resulting pathophysiology is unknown. Therefore, we have generated mouse models of this disease. In addition to exhibiting loss of Mmachc, metabolic perturbations, and developmental defects previously observed in cblC, we uncovered reduced expression of target genes that encode ribosome protein subunits. We also identified specific phenotypes that we ascribe to deregulation of ribosome biogenesis impacting normal translation during development. These findings identify HCFC1/RONIN as transcriptional regulators of ribosome biogenesis during development and their mutation results in complex syndromes exhibiting aspects of both cblC and ribosomopathies.

Bacon: a comprehensive computational benchmarking framework for evaluating targeted chromatin conformation capture-specific methodologies.

Chromatin conformation capture (3C)-based technologies have enabled the accurate detection of topological genomic interactions, and the adoption of ChIP techniques to 3C-based protocols makes it possible to identify long-range interactions. To analyze these large and complex datasets, computational methods are undergoing rapid and expansive evolution. Thus, a thorough evaluation of these analytical pipelines is necessary to identify which commonly used algorithms and processing pipelines need to be improved. Here we present a comprehensive benchmark framework, Bacon, to evaluate the performance of several computational methods. Finally, we provide practical recommendations for users working with HiChIP and/or ChIA-PET analyses.

Oxytocin signaling is necessary for synaptic maturation of adult-born neurons.

Neural circuit plasticity and sensory response dynamics depend on forming new synaptic connections. Despite recent advances toward understanding the consequences of circuit plasticity, the mechanisms driving circuit plasticity are unknown. Adult-born neurons within the olfactory bulb have proven to be a powerful model for studying circuit plasticity, providing a broad and accessible avenue into neuron development, migration, and circuit integration. We and others have shown that efficient adult-born neuron circuit integration hinges on presynaptic activity in the form of diverse signaling peptides. Here, we demonstrate a novel oxytocin-dependent mechanism of adult-born neuron synaptic maturation and circuit integration. We reveal spatial and temporal enrichment of oxytocin receptor expression within adult-born neurons in the murine olfactory bulb, with oxytocin receptor expression peaking during activity-dependent integration. Using viral labeling, confocal microscopy, and cell type-specific RNA-seq, we demonstrate that oxytocin receptor signaling promotes synaptic maturation of newly integrating adult-born neurons by regulating their morphological development and expression of mature synaptic AMPARs and other structural proteins.

The histone H3.3 chaperone HIRA restrains erythroid-biased differentiation of adult hematopoietic stem cells.

Histone variants contribute to the complexity of the chromatin landscape and play an integral role in defining DNA domains and regulating gene expression. The histone H3 variant H3.3 is incorporated into genic elements independent of DNA replication by its chaperone HIRA. Here we demonstrate that Hira is required for the self-renewal of adult hematopoietic stem cells (HSCs) and to restrain erythroid differentiation. Deletion of Hira led to rapid depletion of HSCs while differentiated hematopoietic cells remained largely unaffected. Depletion of HSCs after Hira deletion was accompanied by increased expression of bivalent and erythroid genes, which was exacerbated upon cell division and paralleled increased erythroid differentiation. Assessing H3.3 occupancy identified a subset of polycomb-repressed chromatin in HSCs that depends on HIRA to maintain the inaccessible, H3.3-occupied state for gene repression. HIRA-dependent H3.3 incorporation thus defines distinct repressive chromatin that represses erythroid differentiation of HSCs.

A novel transgenic Cre allele to label mouse cardiac conduction system.

The cardiac conduction system is a network of heterogeneous cell population that initiates and propagates electric excitations in the myocardium. Purkinje fibers, a network of specialized myocardial cells, comprise the distal end of the conduction system in the ventricles. The developmental origins of Purkinje fibers and their roles during cardiac physiology and arrhythmia have been reported. However, it is not clear if they play a role during ischemic injury and heart regeneration. Here we introduce a novel tamoxifen-inducible Cre allele that specifically labels a broad range of components in the cardiac conduction system while excludes other cardiac cell types and vital organs. Using this new allele, we investigated the cellular and molecular response of Purkinje fibers to myocardial injury. In a neonatal mouse myocardial infarction model, we observed significant increase in Purkinje cell number in regenerating myocardium. RNA-Seq analysis using laser-captured Purkinje fibers showed a unique transcriptomic response to myocardial infarction. Our finds suggest a novel role of cardiac Purkinje fibers in heart injury.

Epigenetic Assays in Purified Cardiomyocyte Nuclei.

The adult mammalian heart's potential for regeneration is very inefficient. Importantly, adult mammalian cardiomyocytes (CMs) are characterized as a cell population with very limited mitotic potential. Conversely, the neonatal mouse heart possesses a brief, yet robust, regenerative capacity within the first week of life. Cell type-specific enrichment procedures are essential for characterizing the full spectrum of epigenomic landscapes and gene regulatory networks deployed by mammalian CMs. In this chapter, we describe a protocol useful for purifying CM nuclei from mammalian cardiac tissue. Furthermore, we detail a low-input procedure suitable for the parallel genome-wide profiling of chromatin accessibility, histone modifications, and transcription factor-binding sites. The CM nuclei purified using this process are suitable for multi-omic profiling approaches.

Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model.

Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-associated risk variants. Modern proximity-ligation assays, like HiChIP and ChIA-PET, facilitate the accurate identification of long-range contacts between enhancers and promoters. However, these assays are technically challenging, expensive, and time-consuming, making it difficult to investigate enhancer topologies, especially in uncharacterized cell types. To overcome these shortcomings, we therefore designed LoopPredictor, an ensemble machine learning model, to predict genome topology for cell types which lack long-range contact maps. To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. Moreover, the integration of several related multi-omics features facilitated identifying and annotating the predicted loops. LoopPredictor is able to efficiently identify cell type-specific enhancer-mediated loops, and promoter-promoter interactions, with a modest feature input requirement. Comparable to experimentally generated H3K27ac HiChIP data, we found that LoopPredictor was able to identify functional enhancer loops. Furthermore, to explore the cross-species prediction capability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and found that the predicted enhancer loops outputs were highly conserved. LoopPredictor enables the dissection of cell type-specific long-range gene regulation and can accelerate the identification of distal disease-associated risk variants.

Long-range Pitx2c enhancer-promoter interactions prevent predisposition to atrial fibrillation.

Genome-wide association studies found that increased risk for atrial fibrillation (AF), the most common human heart arrhythmia, is associated with noncoding sequence variants located in proximity to PITX2 Cardiomyocyte-specific epigenomic and comparative genomics uncovered 2 AF-associated enhancers neighboring PITX2 with varying conservation in mice. Chromosome conformation capture experiments in mice revealed that the Pitx2c promoter directly contacted the AF-associated enhancer regions. CRISPR/Cas9-mediated deletion of a 20-kb topologically engaged enhancer led to reduced Pitx2c transcription and AF predisposition. Allele-specific chromatin immunoprecipitation sequencing on hybrid heterozygous enhancer knockout mice revealed that long-range interaction of an AF-associated region with the Pitx2c promoter was required for maintenance of the Pitx2c promoter chromatin state. Long-range looping was mediated by CCCTC-binding factor (CTCF), since genetic disruption of the intronic CTCF-binding site caused reduced Pitx2c expression, AF predisposition, and diminished active chromatin marks on Pitx2 AF risk variants located at 4q25 reside in genomic regions possessing long-range transcriptional regulatory functions directed at PITX2.

Identification of atrial fibrillation associated genes and functional non-coding variants.

Disease-associated genetic variants that lie in non-coding regions found by genome-wide association studies are thought to alter the functionality of transcription regulatory elements and target gene expression. To uncover causal genetic variants, variant regulatory elements and their target genes, here we cross-reference human transcriptomic, epigenomic and chromatin conformation datasets. Of 104 genetic variant regions associated with atrial fibrillation candidate target genes are prioritized. We optimize EMERGE enhancer prediction and use accessible chromatin profiles of human atrial cardiomyocytes to more accurately predict cardiac regulatory elements and identify hundreds of sub-threshold variants that co-localize with regulatory elements. Removal of mouse homologues of atrial fibrillation-associated regions in vivo uncovers a distal regulatory region involved in Gja1 (Cx43) expression. Our analyses provide a shortlist of genes likely affected by atrial fibrillation-associated variants and provide variant regulatory elements in each region that link genetic variation and target gene regulation, helping to focus future investigations.

Tet inactivation disrupts YY1 binding and long-range chromatin interactions during embryonic heart development.

Tet-mediated DNA demethylation plays an important role in shaping the epigenetic landscape and chromatin accessibility to control gene expression. While several studies demonstrated pivotal roles of Tet in regulating embryonic development, little is known about their functions in heart development. Here we analyze DNA methylation and hydroxymethylation dynamics during early cardiac development in both human and mice. We find that cardiac-specific deletion of Tet2 and Tet3 in mice (Tet2/3-DKO) leads to ventricular non-compaction cardiomyopathy (NCC) with embryonic lethality. Single-cell RNA-seq analyses reveal a reduction in cardiomyocyte numbers and transcriptional reprogramming in cardiac tissues upon Tet2/3 depletion. Impaired DNA demethylation and reduced chromatin accessibility in Tet2/3-DKO mice further compromised Ying-yang1 (YY1) binding to its genomic targets, and perturbed high-order chromatin organization at key genes involved in heart development. Our studies provide evidence of the physiological role of Tet in regulating DNA methylation dynamics and chromatin organization during early heart development.

Hippo pathway deletion in adult resting cardiac fibroblasts initiates a cell state transition with spontaneous and self-sustaining fibrosis.

Cardiac fibroblasts (CFs) respond to injury by transitioning through multiple cell states, including resting CFs, activated CFs, and myofibroblasts. We report here that Hippo signaling cell-autonomously regulates CF fate transitions and proliferation, and non-cell-autonomously regulates both myeloid and CF activation in the heart. Conditional deletion of Hippo pathway kinases, Lats1 and Lats2, in uninjured CFs initiated a self-perpetuating fibrotic response in the adult heart that was exacerbated by myocardial infarction (MI). Single cell transcriptomics showed that uninjured Lats1/2 mutant CFs spontaneously transitioned to a myofibroblast cell state. Through gene regulatory network reconstruction, we found that Hippo-deficient myofibroblasts deployed a network of transcriptional regulators of endoplasmic reticulum (ER) stress, and the unfolded protein response (UPR) consistent with elevated secretory activity. We observed an expansion of myeloid cell heterogeneity in uninjured Lats1/2 CKO hearts with similarity to cells recovered from control hearts post-MI. Integrated genome-wide analysis of Yap chromatin occupancy revealed that Yap directly activates myofibroblast cell identity genes, the proto-oncogene Myc, and an array of genes encoding pro-inflammatory factors through enhancer-promoter looping. Our data indicate that Lats1/2 maintain the resting CF cell state through restricting the Yap-induced injury response.

SOX11 and SOX4 drive the reactivation of an embryonic gene program during murine wound repair.

Tissue injury induces changes in cellular identity, but the underlying molecular mechanisms remain obscure. Here, we show that upon damage in a mouse model, epidermal cells at the wound edge convert to an embryonic-like state, altering particularly the cytoskeletal/extracellular matrix (ECM) components and differentiation program. We show that SOX11 and its closest relative SOX4 dictate embryonic epidermal state, regulating genes involved in epidermal development as well as cytoskeletal/ECM organization. Correspondingly, postnatal induction of SOX11 represses epidermal terminal differentiation while deficiency of Sox11 and Sox4 accelerates differentiation and dramatically impairs cell motility and re-epithelialization. Amongst the embryonic genes reactivated at the wound edge, we identify fascin actin-bundling protein 1 (FSCN1) as a critical direct target of SOX11 and SOX4 regulating cell migration. Our study identifies the reactivated embryonic gene program during wound repair and demonstrates that SOX11 and SOX4 play a central role in this process.