The Transcription Factor REST Is Lost in Aggressive Breast Cancer

The function of the tumor suppressor RE1 silencing transcription factor (REST) is lost in colon and small cell lung cancers and is known to induce anchorage-independent growth in human mammary epithelial cells. However, nothing is currently known about the role of this tumor suppressor in breast cancer. Here, we test the hypothesis that loss of REST function plays a role in breast cancer. To assay breast tumors for REST function, we developed a 24-gene signature composed of direct targets of the transcriptional repressor. Using the 24- gene signature, we identified a previously undefined RESTless breast tumor subtype. Using gene set enrichment analysis, we confirmed the aberrant expression of REST target genes in the REST–less tumors, including neuronal gene targets of REST that are normally not expressed outside the nervous system. Examination of REST mRNA identified a truncated splice variant of REST present in the REST–less tumor population, but not other tumors. Histological analysis of 182 outcome-associated breast tumor tissues also identified a subpopulation of tumors that lack full-length, functional REST and over-express the neuroendocrine marker and REST target gene Chromogranin A. Importantly, patients whose tumors were found to be REST–less using either the 24-gene signature or histology had significantly poorer prognosis and were more than twice as likely to undergo disease recurrence within the first 3 years after diagnosis. We show here that REST function is lost in breast cancer, at least in part via an alternative splicing mechanism. Patients with REST–less breast cancer undergo significantly more early disease recurrence than those with fully functional REST, regardless of estrogen receptor or HER2 status. Importantly, REST status may serve as a predictor of poor prognosis, helping to untangle the heterogeneity inherent in disease course and response to treatment. Additionally, the alternative splicing observed in REST–less breast cancer is an attractive therapeutic target

Metabolic Regulation of Neuronal Plasticity by the Energy Sensor AMPK

Long Term Potentiation (LTP) is a leading candidate mechanism for learning and memory and is also thought to play a role in the progression of seizures to intractable epilepsy. Maintenance of LTP requires RNA transcription, protein translation and signaling through the mammalian Target of Rapamycin (mTOR) pathway. In peripheral tissue, the energy sensor AMP-activated Protein Kinase (AMPK) negatively regulates the mTOR cascade upon glycolytic inhibition and cellular energy stress. We recently demonstrated that the glycolytic inhibitor 2-deoxy-D-glucose (2DG) alters plasticity to retard epileptogenesis in the kindling model of epilepsy. Reduced kindling progression was associated with increased recruitment of the nuclear metabolic sensor CtBP to NRSF at the BDNF promoter. Given that energy metabolism controls mTOR through AMPK in peripheral tissue and the role of mTOR in LTP in neurons, we asked whether energy metabolism and AMPK control LTP. Using a combination of biochemical approaches and field-recordings in mouse hippocampal slices, we show that the master regulator of energy homeostasis, AMPK couples energy metabolism to LTP expression. Administration of the glycolytic inhibitor 2-deoxy-D-glucose (2DG) or the mitochondrial toxin and anti-Type II Diabetes drug, metformin, or AMP mimetic AICAR results in activation of AMPK, repression of the mTOR pathway and prevents maintenance of Late-Phase LTP (L-LTP). Inhibition of AMPK by either compound-C or the ATP mimetic ara-A rescues the suppression of L-LTP by energy stress. We also show that enhanced LTP via AMPK inhibition requires mTOR signaling. These results directly link energy metabolism to plasticity in the mammalian brain and demonstrate that AMPK is a modulator of LTP. Our work opens up the possibility of using modulators of energy metabolism to control neuronal plasticity in diseases and conditions of aberrant plasticity such as epilepsy

Enhanced immunoprecipitation techniques for the identification of RNA-binding protein partners: IGF2BP1 interactions in mammary epithelial cells

RNA-binding proteins (RBPs) regulate the expression of large cohorts of RNA species to produce programmatic changes in cellular phenotypes. To describe the function of RBPs within a cell, it is key to identify their mRNA-binding partners. This is often done by crosslinking nucleic acids to RBPs, followed by chemical release of the nucleic acid fragments for analysis. However, this methodology is lengthy, which involves complex processing with attendant sample losses, thus large amounts of starting materials and prone to artifacts. To evaluate potential alternative technologies, we tested exclusion-based purification of immunoprecipitates (IFAST or SLIDE) and report here that these methods can efficiently, rapidly, and specifically isolate RBP-RNA complexes. The analysis requires less than 1% of the starting material required for techniques that include crosslinking. Depending on the antibody used, 50% to 100% starting protein can be retrieved, facilitating the assay of endogenous levels of RBPs; the isolated ribonucleoproteins are subsequently analyzed using standard techniques, to provide a comprehensive portrait of RBP complexes. Using exclusion-based techniques, we show that the mRNA-binding partners for RBP IGF2BP1 in cultured mammary epithelial cells are enriched in mRNAs important for detoxifying superoxides (specifically glutathione peroxidase [GPX]-1 and GPX-2) and mRNAs encoding mitochondrial proteins. We show that these interactions are functionally significant, as loss of function of IGF2BP1 leads to destabilization of GPX mRNAs and reduces mitochondrial membrane potential and oxygen consumption. We speculate that this underlies a consistent requirement for IGF2BP1 for the expression of clonogenic activity in vitro

A Phenotypic Mouse Model of Basaloid Breast Tumors

Chemotherapeutic strategies that target basal-like breast tumors are difficult to design without understanding their cellular and molecular basis. Here, we induce tumors in mice by carcinogen administration, creating a phenocopy of tumors with the diagnostic and functional aspects of human triple negative disease (including EGFR expression/lack of erbB, estrogen-independent growth and co-clustering of the transcriptome with other basaloid models). These tumor strains are a complement to established mouse models that are based on mutations in Brca1 and/or p53. Tumors comprise two distinct cell subpopulations, basal and luminal epithelial cells. These cell fractions were purified by flow cytometry, and only basal cell fractions found to have tumor initiating activity (cancer stem cells). The phenotype of serially regenerated tumors was stable, and irrespective of tumor precursor cell. Tumors were passaged entirely in vivo and serial generations tested for their phenotypic stability. The relative chemo-sensitivity of basal and luminal cells were evaluated. Upon treatment with anthracycline, tumors were effectively de-bulked, but recurred; this correlated with maintenance of a high rate of basal cell division throughout the treatment period. Thus, these tumors grow as robust cell mixtures of basal bipotential tumor initiating cells alongside a luminal majority, and the cellular response to drug administration is dominated by the distinct biology of the two cell types. Given the ability to separate basal and luminal cells, and the discovery potential of this approach, we propose that this mouse model could be a convenient one for preclinical studies

MAGIC requires a valid background gene list for optimal performance.

(A) D(r)-r curves for the 4 datasets generated for MAGIC outputs in the presence or absence of a background list. Kolmogorov-smirnov statistics for the 2 curves: MCF7(shCon_vs_shREST); D = 0.10, p = 8.6x10-5. TCGA(Lum_vs_Basal); D = 0.15, p = 4.6x10-11. Brain(WT_vs_CTCFko); D = 0.26, p = 1.1x10-16. DGC(Quiet_vs_Reactive); D = 0.11, p = 3.9x10-9. (B) Precision Recall curves for MAGIC outputs in the presence or absence of a background list: MCF7(shCon_vs_shREST); 0.84 vs 0.78, TCGA(Lum_vs_Basal); 0.83 vs 0.81, Brain(WT_vs_CTCFko); 0.91 vs 0.71, DGC(Quiet_vs_Reactive); 0.72 vs 0.67. Black vertical line denotes 80% Recall (C) Receiver Operator Characteristics curves for MAGIC outputs in the presence or absence of a background list (unbalanced). (D) Emperical cumulative distribution for False Discovery Rates associated with MAGIC outputs in the presence or absence of a background list. For all datasets, Kolmogorov-smirnov p −4. (E) The integer ranks for the top 50 Factors called by MAGIC in the presence of a background list were compared to their ranks in the absence of a background list.</p

MAGIC demonstrates skill at calling manipulated factors as assessed by Precision Recall and Receiver Operator Characteristics.

(A) Precision Recall curves for the four datasets and all algorithms and libraries. (B) Receiver Operator Characteristic curves for the four datasets and all algorithms and libraries. As in panel A, data was not balanced prior to graphing. (C) ROC versus PR AUCs for all algorithms and libraries. (D) PR AUCs were scaled for fractional rank of the manipulated factor by multiplying PR UAC by FR.</p

Manipulated transcription factors and associated cofactors are preferentially ranked by MAGIC compared to CHEA3, TFEA and Enrichr.

(A) Emperical cumulatives were generated of factional ranks (1/Integer ranks) for manipulated factors and associated cofactors for the 4 datasets using all algorithms and libraries as in Fig 1. The difference between the cumulative of all scaled fractional ranks and a uniform distribution for the manipulated factor and associated cofactors is plotted against the fractional rank. Kolmogorov-smirnov tests of each distribution against a uniform distribution yields p−10 for all tests. (B) Area Under Curve (AUC) for D(r)-r x r curves in panel A. (C) The D(r)-r curves in panel A were scaled for the rank of the manipulated actor. For each algorithm, D(r)-r was multiplied by the fractional rank of the manipulated factor (FR). (D) AUCs for curves in panel C.</p

MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data

Transcriptomic profiling is an immensely powerful hypothesis generating tool. However, accurately predicting the transcription factors (TFs) and cofactors that drive transcriptomic differences between samples is challenging. A number of algorithms draw on ChIP-seq tracks to define TFs and cofactors behind gene changes. These approaches assign TFs and cofactors to genes via a binary designation of ‘target’, or ‘non-target’ followed by Fisher Exact Tests to assess enrichment of TFs and cofactors. ENCODE archives 2314 ChIP-seq tracks of 684 TFs and cofactors assayed across a 117 human cell lines under a multitude of growth and maintenance conditions. The algorithm presented herein, Mining Algorithm for GenetIc Controllers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an a priori binary classification of genes as targets or non-targets. When compared to other TF mining resources, MAGIC displayed favourable performance in predicting TFs and cofactors that drive gene changes in 4 settings: 1) A cell line expressing or lacking single TF, 2) Breast tumors divided along PAM50 designations 3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype 4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels. In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.</div