NR4A1 and NR4A3 restrict HSC proliferation via reciprocal regulation of C/EBPα and inflammatory signaling.

Members of the NR4A subfamily of nuclear receptors have complex, overlapping roles during hematopoietic cell development and also function as tumor suppressors of hematologic malignancies. We previously identified NR4A1 and NR4A3 (NR4A1/3) as functionally redundant suppressors of acute myeloid leukemia (AML) development. However, their role in hematopoietic stem cell (HSC) homeostasis remains to be disclosed. Using a conditional Nr4a1/Nr4a3 knockout mouse (CDKO), we show that codepletion of NR4A1/3 promotes acute changes in HSC homeostasis including loss of HSC quiescence, accumulation of oxidative stress, and DNA damage while maintaining stem cell regenerative and differentiation capacity. Molecular profiling of CDKO HSCs revealed widespread upregulation of genetic programs governing cell cycle and inflammation and an aberrant activation of the interferon and NF-κB signaling pathways in the absence of stimuli. Mechanistically, we demonstrate that NR4A1/3 restrict HSC proliferation in part through activation of a C/EBPα-driven antiproliferative network by directly binding to a hematopoietic-specific Cebpa enhancer and activating Cebpa transcription. In addition, NR4A1/3 occupy the regulatory regions of NF-κB-regulated inflammatory cytokines, antagonizing the activation of NF-κB signaling. Taken together, our results reveal a novel coordinate control of HSC quiescence by NR4A1/3 through direct activation of C/EBPα and suppression of activation of NF-κB-driven proliferative inflammatory responses.

Exposing the cancer genome atlas as a SPARQL endpoint.

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to integrate and discover data with those characteristics, namely through exposure of data as Web services supporting SPARQL, the Resource Description Framework query language. Most SPARQL endpoints, however, cannot easily be queried by data experts. Furthermore, exposing experimental data as SPARQL endpoints remains a challenging task because, in most cases, data must first be converted to Resource Description Framework triples. In line with those requirements, we have developed an infrastructure to expose clinical, demographic and molecular data elements generated by TCGA as a SPARQL endpoint by assigning elements to entities of the Simple Sloppy Semantic Database (S3DB) management model. All components of the infrastructure are available as independent Representational State Transfer (REST) Web services to encourage reusability, and a simple interface was developed to automatically assemble SPARQL queries by navigating a representation of the TCGA domain. A key feature of the proposed solution that greatly facilitates assembly of SPARQL queries is the distinction between the TCGA domain descriptors and data elements. Furthermore, the use of the S3DB management model as a mediator enables queries to both public and protected data without the need for prior submission to a single data source.

Targeting Oncogenic Super Enhancers in MYC-Dependent AML Using a Small Molecule Activator of NR4A Nuclear Receptors.

Epigenetic reprogramming in Acute Myeloid Leukemia (AML) leads to the aberrant activation of super enhancer (SE) landscapes that drive the expression of key oncogenes, including the oncogenic MYC pathway. These SEs have been identified as promising therapeutic targets, and have given rise to a new class of drugs, including BET protein inhibitors, which center on targeting SE activity. NR4A nuclear receptors are tumor suppressors of AML that function in part through transcriptional repression of the MYC-driven oncogenic program via mechanisms that remain unclear. Here we show that NR4A1, and the NR4A inducing drug dihydroergotamine (DHE), regulate overlapping gene expression programs in AML and repress transcription of a subset of SE-associated leukemic oncogenes, including MYC. NR4As interact with an AML-selective SE cluster that governs MYC transcription and decommissions its activation status by dismissing essential SE-bound coactivators including BRD4, Mediator and p300, leading to loss of p300-dependent H3K27 acetylation and Pol 2-dependent eRNA transcription. DHE shows similar efficacy to the BET inhibitor JQ1 at repressing SE-dependent MYC expression and AML growth in mouse xenografts. Thus, DHE induction of NR4As provides an alternative strategy to BET inhibitors to target MYC dependencies via suppression of the AML-selective SE governing MYC expression.

Drug targeting of NR4A nuclear receptors for treatment of acute myeloid leukemia.

NR4As are AML tumor suppressors that are frequently silenced in human acute myeloid leukemia (AML). Despite their potential as novel targets for therapeutic intervention, mechanisms of NR4A silencing and strategies for their reactivation remain poorly defined. Here we show that NR4A silencing in AML occurs through blockade of transcriptional elongation rather than epigenetic promoter silencing. By intersection of NR4A-regulated gene signatures captured upon acute, exogenous expression of NR4As in human AML cells with in silico chemical genomics screening, we identify several FDA-approved drugs including dihydroergotamine (DHE) that reactivate NR4A expression and regulate NR4A-dependent gene signatures. We show that DHE induces NR4A expression via recruitment of the super elongation complex to enable elongation of NR4A promoter paused RNA polymerase II. Finally, DHE exhibits AML selective NR4A-dependent anti-leukemic activity in cytogenetically distinct human AML cells in vitro and delays AML progression in mice revealing its potential as a novel therapeutic agent in AML.

Single-cell analysis of transcription kinetics across the cell cycle.

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.

Molecular phylogeny of penaeid shrimps inferred from two mitochondrial markers.

Penaeid shrimps are an important resource in crustacean fisheries, representing more than the half of the gross production of shrimp worldwide. In the present study, we used a sample of wide-ranging diversity (41 shrimp species) and two mitochondrial markers (758 bp) to clarify the evolutionary relationships among Penaeidae genera. Three different methodologies of tree reconstruction were employed in the study: maximum likelihood, neighbor joining and Bayesian analysis. Our results suggest that the old Penaeus genus is monophyletic and that the inclusion of the Solenocera genus within the Penaeidae family remains uncertain. With respect to Metapenaeopsis monophyly, species of this genus appeared clustered, but with a nonsignificant bootstrap value. These results elucidate some features of the unclear evolution of Penaeidae and may contribute to the taxonomic characterization of this family.

Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis.

BACKGROUND: The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM). METHODS: Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant. RESULTS: To identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively). We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, GSE4290 dataset). An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables. CONCLUSIONS: By integrating multidimensional genomic data from TCGA, we identified a specific survival model in a new prognostic group of GBM and suggest that molecular stratification of patients with GBM into homogeneous subgroups may provide opportunities for the development of new treatment modalities.