Taken together, the data suggest the miRNA34s might be key effectors of p53 tumor-suppressor function, and their inactivation might contribute to certain cancers.

Men who develop metastatic castration-resistant prostate cancer (CRPC) invariably succumb to the disease. The development and progression to CRPC following androgen ablation therapy is predominantly driven by unregulated androgen receptor (AR) signaling1-3. Despite the success of recently approved therapies targeting AR signaling such as abiraterone4-6 and second generation anti-androgens MDV3100 (enzalutamide)7,8, durable responses are limited, presumably due to acquired resistance. Recently JQ1 and I-BET, two selective small molecule inhibitors that target the amino-terminal bromodomains of BRD4, have been shown to exhibit anti-proliferative effects in a range of malignancies9-12. Here we show that AR signaling-competent CRPC cell lines are preferentially sensitive to BET bromodomain inhibition. BRD4 physically interacts with the N-terminal domain of AR and can be disrupted by JQ111,13. Like the direct AR antagonist, MDV3100, JQ1 disrupted AR recruitment to target gene loci. In contrast to MDV3100, JQ1 functions downstream of AR, and more potently abrogated BRD4 localization to AR target loci and AR-mediated gene transcription including induction of TMPRSS2-ERG and its oncogenic activity. In vivo, BET bromodomain inhibition was more efficacious than direct AR antagonism in CRPC xenograft models. Taken together, these studies provide a novel epigenetic approach for the concerted blockade of oncogenic drivers in advanced prostate cancer.

SUMMARY While chromosomal rearrangements fusing the androgen-regulated gene TMPRSS2 to the oncogenic ETS transcription factor ERG occur in approximately 50% of prostate cancers, how the fusion products regulate prostate cancer remains unclear. Using chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq), we found that ERG disrupts androgen receptor (AR) signaling by inhibiting AR expression, binding to and inhibiting AR activity at gene-specific loci, and inducing repressive epigenetic programs via direct activation of the H3K27 methyltransferase EZH2, a Polycomb group protein. These findings provide a working model in which TMPRSS2-ERG plays a critical role in cancer progression by disrupting lineage-specific differentiation of the prostate and potentiating the EZH2-mediated de-differentiation program.

We present SAINT (Significance Analysis of INTeractome), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity-purification coupled to mass spectrometry (AP-MS). The method utilizes label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We demonstrate that SAINT is applicable to data of different scales and protein connectivity and allows for the transparent analysis of AP-MS data.

SUMMARY The mechanisms responsible for the establishment of physical domains in metazoan chromosomes are poorly understood. Here we find that physical domains in Drosophila chromosomes are demarcated at regions of active transcription and high gene density that are enriched for transcription factors and specific combinations of insulator proteins. Physical domains contain different types of chromatin defined by the presence of specific proteins and epigenetic marks, with active chromatin preferentially located at the borders and silenced chromatin in the interior. Domain boundaries participate in long-range interactions that may contribute to the clustering of regions of active or silenced chromatin in the nucleus. Analysis of transgenes suggests that chromatin is more accessible and permissive to transcription at the borders than inside domains, independent of the presence of active or silencing histone modifications. These results suggest that the higher-order physical organization of chromatin may impose an additional level of regulation over classical epigenetic marks.

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.

Cancer genomic, transcriptomic, and proteomic profiling has generated extensive data that necessitate the development of tools for its analysis and dissemination. We developed UALCAN to provide a portal for easy exploring, analyzing, and visualizing these data, allowing users to integrate the data to better understand the gene, proteins, and pathways perturbed in cancer and make discoveries. UALCAN web portal enables analyzing and delivering cancer transcriptome, proteomics, and patient survival data to the cancer research community. With data obtained from The Cancer Genome Atlas (TCGA) project, UALCAN has enabled users to evaluate protein-coding gene expression and its impact on patient survival across 33 types of cancers. The web portal has been used extensively since its release and received immense popularity, underlined by its usage from cancer researchers in more than 100 countries. The present manuscript highlights the task we have undertaken and updates that we have made to UALCAN since its release in 2017. Extensive user feedback motivated us to expand the resource by including data on a) microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and promoter DNA methylation from TCGA and b) mass spectrometry-based proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). UALCAN provides easy access to pre-computed, tumor subgroup-based gene/protein expression, promoter DNA methylation status, and Kaplan-Meier survival analyses. It also provides new visualization features to comprehend and integrate observations and aids in generating hypotheses for testing. UALCAN is accessible at http://ualcan.path.uab.edu

Haplotypes have gained increasing attention in the mapping of complex-disease genes, because of the abundance of single-nucleotide polymorphisms (SNPs) and the limited power of conventional single-locus analyses. It has been shown that haplotype-inference methods such as Clark's algorithm, the expectation-maximization algorithm, and a coalescence-based iterative-sampling algorithm are fairly effective and economical alternatives to molecular-haplotyping methods. To contend with some weaknesses of the existing algorithms, we propose a new Monte Carlo approach. In particular, we first partition the whole haplotype into smaller segments. Then, we use the Gibbs sampler both to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can accurately and rapidly infer haplotypes for a large number of linked SNPs. By using a wide variety of real and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots.