The pharmacological inhibition of general transcriptional regulators has the potential to block growth through targeting multiple tumorigenic signaling pathways simultaneously. Here, using an innovative cell-based screen, we identify a structurally unique small molecule (named JIB-04) which specifically inhibits the activity of the Jumonji family of histone demethylases in vitro, in cancer cells, and in tumors in vivo. Unlike known inhibitors, JIB-04 is not a competitive inhibitor of α-ketoglutarate. In cancer but not in patient-matched normal cells, JIB-04 alters a subset of transcriptional pathways and blocks viability. In mice, JIB-04 reduces tumor burden and prolongs survival. Importantly, we find that patients with breast tumors that overexpress Jumonji demethylases have significantly lower survival. Thus JIB-04, a novel inhibitor of Jumonji demethylases in vitro and in vivo, constitutes a unique potential therapeutic and research tool against cancer, and validates the use of unbiased cellular screens to discover chemical modulators with disease relevance.

Although histone deacetylases (HDACs) are generally viewed as corepressors, we show that HDAC1 serves as a coactivator for the glucocorticoid receptor (GR). Furthermore, a subfraction of cellular HDAC1 is acetylated after association with the GR, and this acetylation event correlates with a decrease in promoter activity. HDAC1 in repressed chromatin is highly acetylated, while the deacetylase found on transcriptionally active chromatin manifests a low level of acetylation. Acetylation of purified HDAC1 inactivates its deacetylase activity, and mutation of the critical acetylation sites abrogates HDAC1 function in vivo. We propose that hormone activation of the receptor leads to progressive acetylation of HDAC1 in vivo, which in turn inhibits the deacetylase activity of the enzyme and prevents a deacetylation event that is required for promoter activation. These findings indicate that HDAC1 is required for the induction of some genes by the GR, and this activator function is dynamically modulated by acetylation.

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Cellular populations have been widely observed to respond heterogeneously to perturbation. However, interpreting the observed heterogeneity is an extremely challenging problem because of the complexity of possible cellular phenotypes, the large dimension of potential perturbations, and the lack of methods for separating meaningful biological information from noise. Here, we develop an image-based approach to characterize cellular phenotypes based on patterns of signaling marker colocalization. Heterogeneous cellular populations are characterized as mixtures of phenotypically distinct subpopulations, and responses to perturbations are summarized succinctly as probabilistic redistributions of these mixtures. We apply our method to characterize the heterogeneous responses of cancer cells to a panel of drugs. We find that cells treated with drugs of (dis-)similar mechanism exhibit (dis-)similar patterns of heterogeneity. Despite the observed phenotypic diversity of cells observed within our data, low-complexity models of heterogeneity were sufficient to distinguish most classes of drug mechanism. Our approach offers a computational framework for assessing the complexity of cellular heterogeneity, investigating the degree to which perturbations induce redistributions of a limited, but nontrivial, repertoire of underlying states and revealing functional significance contained within distinct patterns of heterogeneous responses. automated microscopy ͉ cellular heterogeneity ͉ image analysis