According to current models, once the cell has reached terminal differentiation, the enhancer repertoire is completely established and maintained by cooperatively acting lineage-specific transcription factors (TFs). TFs activated by extracellular stimuli operate within this predetermined repertoire, landing close to where master regulators are constitutively bound. Here, we describe latent enhancers, defined as regions of the genome that in terminally differentiated cells are unbound by TFs and lack the histone marks characteristic of enhancers but acquire these features in response to stimulation. Macrophage stimulation caused sequential binding of stimulus-activated and lineage-determining TFs to these regions, enabling deposition of enhancer marks. Once unveiled, many of these enhancers did not return to a latent state when stimulation ceased; instead, they persisted and mediated a faster and stronger response upon restimulation. We suggest that stimulus-specific expansion of the cis-regulatory repertoire provides an epigenomic memory of the exposure to environmental agents.

Histone deacetylases (HDACs) regulate inflammatory gene expression, as indicated by the potent antiinflammatory activity of pan-HDAC inhibitors. However, the specific contribution of each of the 11 HDAC proteins to the inflammatory gene expression program is unknown. Using an integrated genomic approach, we found that Hdac3-deficient macrophages were unable to activate almost half of the inflammatory gene expression program when stimulated with LPS. A large part of the activation defect was attributable to loss of basal and LPS-inducible expression of IFN-β, which maintains Stat1 protein levels in unstimulated cells and acts in an autocrine/paracrine manner after stimulation to promote a secondary wave of Stat1-dependent gene expression. Loss of Hdac3-mediated repression of nuclear receptors led to hyperacetylation of thousands of genomic sites and associated gene derepression. The up-regulation of the constitutively expressed prostaglandin endoperoxide synthase, Ptgs1 (Cox-1), a nuclear receptor target, had a causative role in the phenotype because its chemical inhibition reverted, albeit partially, the Ifn-β activation defect. These data indicate a central role for Hdac3 in inflammation and may have relevance for the use of selective Hdac inhibitors as antiinflammatory agents.chromatin | transcription T he inflammatory response involves the differential expression of hundreds of genes and is driven by well-defined stimulusregulated transcription factors [e.g., NF-κB, activator protein-1 (AP-1), IFN regulatory factors (IRFs)] (1, 2). The interplay between these factors and the regulatory landscape specific to each cell type, which is generated by lineage-determining transcription factors, affects the final transcriptional output and the identity of the genes regulated by inflammatory stimuli (3).

Background: Inflammation is a key component of cardiac disease, with macrophages and T lymphocytes mediating essential roles in the progression to heart failure. Nonetheless, little insight exists on other immune subsets involved in the cardiotoxic response. Methods: Here, we used single-cell RNA sequencing to map the cardiac immune composition in the standard murine nonischemic, pressure-overload heart failure model. By focusing our analysis on CD45 + cells, we obtained a higher resolution identification of the immune cell subsets in the heart, at early and late stages of disease and in controls. We then integrated our findings using multiparameter flow cytometry, immunohistochemistry, and tissue clarification immunofluorescence in mouse and human. Results: We found that most major immune cell subpopulations, including macrophages, B cells, T cells and regulatory T cells, dendritic cells, Natural Killer cells, neutrophils, and mast cells are present in both healthy and diseased hearts. Most cell subsets are found within the myocardium, whereas mast cells are found also in the epicardium. Upon induction of pressure overload, immune activation occurs across the entire range of immune cell types. Activation led to upregulation of key subset-specific molecules, such as oncostatin M in proinflammatory macrophages and PD-1 in regulatory T cells, that may help explain clinical findings such as the refractivity of patients with heart failure to anti–tumor necrosis factor therapy and cardiac toxicity during anti–PD-1 cancer immunotherapy, respectively. Conclusions: Despite the absence of infectious agents or an autoimmune trigger, induction of disease leads to immune activation that involves far more cell types than previously thought, including neutrophils, B cells, Natural Killer cells, and mast cells. This opens up the field of cardioimmunology to further investigation by using toolkits that have already been developed to study the aforementioned immune subsets. The subset-specific molecules that mediate their activation may thus become useful targets for the diagnostics or therapy of heart failure.

Fibro-adipogenic progenitors (FAPs) are important components of the skeletal muscle regenerative environment. Whether FAPs support muscle regeneration or promote fibro-adipogenic degeneration is emerging as a key determinant in the pathogenesis of muscular diseases, including Duchenne muscular dystrophy (DMD). However, the molecular mechanism that controls FAP lineage commitment and activity is currently unknown. We show here that an HDAC-myomiR-BAF60 variant network regulates the fate of FAPs in dystrophic muscles of mdx mice. Combinatorial analysis of gene expression microarray, genome-wide chromatin remodeling by nuclease accessibility (NA) combined with next-generation sequencing (NA-seq), small RNA sequencing (RNA-seq), and microRNA (miR) high-throughput screening (HTS) against SWI/SNF BAF60 variants revealed that HDAC inhibitors (HDACis) derepress a ''latent'' myogenic program in FAPs from dystrophic muscles at early stages of disease. Specifically, HDAC inhibition induces two core components of the myogenic transcriptional machinery, MYOD and BAF60C, and up-regulates the myogenic miRs (myomiRs) (miR-1.2, miR-133, and miR-206), which target the alternative BAF60 variants BAF60A and BAF60B, ultimately directing promyogenic differentiation while suppressing the fibro-adipogenic phenotype. In contrast, FAPs from late stage dystrophic muscles are resistant to HDACi-induced chromatin remodeling at myogenic loci and fail to activate the promyogenic phenotype. These results reveal a previously unappreciated disease stage-specific bipotency of mesenchimal cells within the regenerative environment of dystrophic muscles. Resolution of such bipotency by epigenetic intervention with HDACis provides a molecular rationale for the in situ reprogramming of target cells to promote therapeutic regeneration of dystrophic muscles.

PURPOSE Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication. METHODS We retrospectively enrolled 2,043 patients. Using Bayesian networks and Dirichlet processes, we combined mutations in 47 genes with cytogenetic abnormalities to identify genetic associations and subgroups. Random-effects Cox proportional hazards multistate modeling was used for developing prognostic models. An independent validation on 318 cases was performed. RESULTS We identify eight MDS groups (clusters) according to specific genomic features. In five groups, dominant genomic features include splicing gene mutations ( SF3B1, SRSF2, and U2AF1) that occur early in disease history, determine specific phenotypes, and drive disease evolution. These groups display different prognosis (groups with SF3B1 mutations being associated with better survival). Specific co-mutation patterns account for clinical heterogeneity within SF3B1- and SRSF2-related MDS. MDS with complex karyotype and/or TP53 gene abnormalities and MDS with acute leukemia–like mutations show poorest prognosis. MDS with 5q deletion are clustered into two distinct groups according to the number of mutated genes and/or presence of TP53 mutations. By integrating 63 clinical and genomic variables, we define a novel prognostic model that generates personally tailored predictions of survival. The predicted and observed outcomes correlate well in internal cross-validation and in an independent external cohort. This model substantially improves predictive accuracy of currently available prognostic tools. We have created a Web portal that allows outcome predictions to be generated for user-defined constellations of genomic and clinical features. CONCLUSION Genomic landscape in MDS reveals distinct subgroups associated with specific clinical features and discrete patterns of evolution, providing a proof of concept for next-generation disease classification and prognosis.