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Alternative polyadenylation generates numerous 3′ mRNA isoforms that can differ in their stability, structure, and function. These isoforms can be used to map mRNA stabilizing and destabilizing elements within 3′ untranslated regions (3′UTRs). Here, we examine how environmental conditions affect 3′ mRNA isoform turnover and structure in yeast cells on a transcriptome scale. Isoform stability broadly increases when cells grow more slowly, with relative half-lives of most isoforms being well correlated across multiple conditions. Surprisingly, dimethyl sulfate probing reveals that individual 3′ isoforms have similar structures across different conditions, in contrast to the extensive structural differences that can exist between closely related isoforms in an individual condition. Unexpectedly, most mRNA stabilizing and destabilizing elements function only in a single growth condition. The genes associated with some classes of condition-specific stability elements are enriched for different functional categories, suggesting that regulated mRNA stability might contribute to adaptation to different growth environments. Condition-specific stability elements do not result in corresponding condition-specific changes in steady-state mRNA isoform levels. This observation is consistent with a compensatory mechanism between polyadenylation and stability, and it suggests that condition-specific mRNA stability elements might largely reflect condition-specific regulation of mRNA 3′ end formation.

Supplementary Figures 1-2 from Inhibition of miR-193a Expression by Max and RXRα Activates K-Ras and PLAU to Mediate Distinct Aspects of Cellular Transformation

Metformin, the first-line drug for treating diabetes, selectively kills the chemotherapy resistant subpopulation of cancer stem cells (CSC) in genetically distinct types of breast cancer cell lines. In mouse xenografts, injection of metformin and the chemotherapeutic drug doxorubicin near the tumor is more effective than either drug alone in blocking tumor growth and preventing relapse. Here, we show that metformin is equally effective when given orally together with paclitaxel, carboplatin, and doxorubicin, indicating that metformin works together with a variety of standard chemotherapeutic agents. In addition, metformin has comparable effects on tumor regression and preventing relapse when combined with a four-fold reduced dose of doxorubicin that is not effective as a monotherapy. Finally, the combination of metformin and doxorubicin prevents relapse in xenografts generated with prostate and lung cancer cell lines. These observations provide further evidence for the CSC hypothesis for cancer relapse, an experimental rationale for using metformin as part of combinatorial therapy in a variety of clinical settings, and for reducing the chemotherapy dose in cancer patients. Cancer Res; 71(9); 3196–201. ©2011 AACR.

Supplementary Figures 1-18, Methods from Akt2 Regulates All Akt Isoforms and Promotes Resistance to Hypoxia through Induction of miR-21 upon Oxygen Deprivation

“Biological noise” is defined as functionally insignificant events that occur in living cells due to imperfect fidelity of biological processes. Distinguishing between biological function and biological noise is often difficult, and experiments to measure biological noise have not been performed. Here, we measure biological noise in yeast cells by analyzing chromatin structure and transcription of an 18 kb region of DNA whose sequence was randomly generated and hence lacks biological function. Nucleosome occupancy on random-sequence DNA is comparable to that on yeast genomic DNA. However, nucleosome-depleted regions are much less frequent, and there are fewer well-positioned nucleosomes and shorter nucleosome arrays. Steady-state levels of RNAs expressed from random-sequence DNA are comparable to those of typical yeast mRNAs, although transcription and mRNA decay rates are higher. Transcriptional initiation (5’ ends) from random-sequence DNA occurs at numerous sites at low levels, indicating very low intrinsic specificity of the Pol II machinery. In contrast, poly(A) profiles (relative levels and clustering of 3’ isoforms) of random-sequence RNAs are roughly comparable to those within 3’ untranslated regions of yeast mRNAs, suggesting limited evolutionary constraints on poly(A) site choice. RNAs expressed from random-sequence DNA show higher cell-to-cell variability than RNAs expressed from yeast genomic DNA, suggesting that functional elements limit the variability among individual cells within a population. These observations indicate that transcriptional noise occurs at high levels in yeast, and they provide insight into how chromatin and transcription patterns arise from the evolved yeast genome.

Alternative polyadenylation yields many mRNA isoforms whose 3’ termini occur disproportionately in clusters within 3’ UTRs. Previously, we showed that profiles of poly(A) site usage are regulated by the rate of transcriptional elongation by RNA polymerase (Pol) II (Geisberg et., 2020). Pol II derivatives with slow elongation rates confer an upstream-shifted poly(A) profile, whereas fast Pol II strains confer a downstream-shifted poly(A) profile. In yeast, upstream and downstream shifts within isoform clusters occur steadily at the nucleotide level. In contrast, changes from one isoform to the next are much smaller between clusters, even when the distances between them are relatively large. GC content in a region 13-30 nt downstream from isoform clusters is linked to Pol II elongation rate. In human cells, the upstream shift caused by a slow Pol II mutant also occurs continuously at the nucleotide level within clusters, but not between them. Pol II occupancy increases just downstream of the most speed-sensitive poly(A) sites, suggesting a linkage between reduced elongation rate and cluster formation. These observations suggest that 1) Pol II elongation speed affects the nucleotide-level dwell time allowing polyadenylation to occur, 2) poly(A) site clusters are linked to the local elongation rate and hence do not arise simply by intrinsically imprecise cleavage and polyadenylation of the RNA substrate, 3) DNA sequence elements can affect Pol II elongation and poly(A) profiles, and 4) the cleavage/polyadenylation and Pol II elongation complexes are spatially, and perhaps physically, coupled so that polyadenylation occurs rapidly upon emergence of the nascent RNA from the Pol II elongation complex.

The 3' ends of eukaryotic mRNAs are generated by cleavage of nascent transcripts followed by polyadenylation, which occurs at numerous sites within 3' untranslated regions (3' UTRs) but rarely within coding regions. An individual gene can yield many 3'-mRNA isoforms with distinct half-lives. We dissect the relative contributions of protein-coding sequences (open reading frames [ORFs]) and 3' UTRs to polyadenylation profiles in yeast. ORF-deleted derivatives often display strongly decreased mRNA levels, indicating that ORFs contribute to overall mRNA stability. Poly(A) profiles, and hence relative isoform half-lives, of most (9 of 10) ORF-deleted derivatives are very similar to their wild-type counterparts. Similarly, in-frame insertion of a large protein-coding fragment between the ORF and 3' UTR has minimal effect on the poly(A) profile in all 15 cases tested. Last, reciprocal ORF/3'-UTR chimeric genes indicate that the poly(A) profile is determined by the 3' UTR. Thus, 3' UTRs are self-contained modular entities sufficient to determine poly(A) profiles and relative 3'-isoform half-lives. In the one atypical instance, ORF deletion causes an upstream shift of poly(A) sites, likely because juxtaposition of an unusually high AT-rich stretch directs polyadenylation closely downstream. This suggests that long AT-rich stretches, which are not encountered until after coding regions, are important for restricting polyadenylation to 3' UTRs.

Significance Cells coordinate the rates of major biological processes in response to environmental conditions such as nutrient availability and temperature. Coordination mechanisms in which effects on one process are compensated for by effects on another process maintain an appropriate cellular balance. Here, we uncover a compensatory link between cleavage/polyadenylation, which generates poly(A) tails at mRNA 3′ ends, and mRNA decay. This compensatory link suggests that during cleavage/polyadenylation in the nucleus, mRNA isoforms are marked in a manner that persists upon translocation to the cytoplasm. This mark affects the activity of mRNA degradation machinery, thus influencing mRNA stability and allowing cells to maintain relatively similar levels of mRNA isoforms even when cleavage/polyadenylation rates change in response to environmental conditions., Cells have compensatory mechanisms to coordinate the rates of major biological processes, thereby permitting growth in a wide variety of conditions. Here, we uncover a compensatory link between cleavage/polyadenylation in the nucleus and messenger RNA (mRNA) turnover in the cytoplasm. On a global basis, same-gene 3′ mRNA isoforms with twofold or greater differences in half-lives have steady-state mRNA levels that differ by significantly less than a factor of 2. In addition, increased efficiency of cleavage/polyadenylation at a specific site is associated with reduced stability of the corresponding 3′ mRNA isoform. This inverse relationship between cleavage/polyadenylation and mRNA isoform half-life reduces the variability in the steady-state levels of mRNA isoforms, and it occurs in all four growth conditions tested. These observations suggest that during cleavage/polyadenylation in the nucleus, mRNA isoforms are marked in a manner that persists upon translocation to the cytoplasm and affects the activity of mRNA degradation machinery, thus influencing mRNA stability.

The preinitiation complex (PIC) for transcriptional initiation by RNA polymerase (Pol) II is composed of general transcription factors that are highly conserved. However, analysis of ChIP-seq datasets reveals kinetic and compositional differences in the transcriptional initiation process among eukaryotic species. In yeast, Mediator associates strongly with activator proteins bound to enhancers, but it transiently associates with promoters in a form that lacks the kinase module. In contrast, in human, mouse, and fly cells, Mediator with its kinase module stably associates with promoters, but not with activator-binding sites. This suggests that yeast and metazoans differ in the nature of the dynamic bridge of Mediator between activators and Pol II and the composition of a stable inactive PIC-like entity. As in yeast, occupancies of TATA-binding protein (TBP) and TBP-associated factors (Tafs) at mammalian promoters are not strictly correlated. This suggests that within PICs, TFIID is not a monolithic entity, and multiple forms of TBP affect initiation at different classes of genes. TFIID in flies, but not yeast and mammals, interacts strongly at regions downstream of the initiation site, consistent with the importance of downstream promoter elements in that species. Lastly, Taf7 and the mammalian-specific Med26 subunit of Mediator also interact near the Pol II pause region downstream of the PIC, but only in subsets of genes and often not together. Species-specific differences in PIC structure and function are likely to affect how activators and repressors affect transcriptional activity.

The YAP and TAZ paralogs are transcriptional co-activators recruited to target sites by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB (a member of the AP-1 family) and STAT3, key transcription factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, and they stimulate transcriptional activation by AP-1 proteins. JUNB, STAT3, and TEAD co-localize at virtually all YAP/TAZ target sites, yet many target sites only contain individual AP-1, TEAD, or STAT3 motifs. This observation and differences in relative crosslinking efficiencies of JUNB, TEAD, and STAT3 at YAP/TAZ target sites suggest that YAP/TAZ is recruited by different forms of an AP-1/STAT3/TEAD complex depending on the recruiting motif. The different classes of YAP/TAZ target sites are associated with largely non-overlapping genes with distinct functions. A small minority of target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes from shared YAP/TAZ target sites. Genes containing either the AP-1 or TEAD class of YAP/TAZ sites are associated with poor survival of breast cancer patients with the triple-negative form of the disease.

Transcription factor binding to target sites in vivo is a dynamic process that involves cycles of association and dissociation, with individual proteins differing in their binding dynamics. The dynamics at individual sites on a genomic scale have been investigated in yeast cells, but comparable experiments have not been done in multicellular eukaryotes. Here, we describe a tamoxifen-inducible, time-course ChIP-seq approach to measure transcription factor binding dynamics at target sites throughout the human genome. As observed in yeast cells, the TATA-binding protein (TBP) typically displays rapid turnover at RNA polymerase (Pol) II-transcribed promoters, slow turnover at Pol III promoters, and very slow turnover at the Pol I promoter. Turnover rates vary widely among Pol II promoters in a manner that does not correlate with the level of TBP occupancy. Human Pol II promoters with slow TBP dissociation preferentially contain a TATA consensus motif, support high transcriptional activity of downstream genes, and are linked with specific activators and chromatin remodelers. These properties of human promoters with slow TBP turnover differ from those of yeast promoters with slow turnover. These observations suggest that TBP binding dynamics differentially affect promoter function and gene expression, possibly at the level of transcriptional reinitiation/bursting.

S100A8/S100A9, a secreted cytokine, also acts in the nucleus as a transcriptional coactivator to promote cellular transformation., Cytokines are extracellular proteins that convey messages between cells by interacting with cognate receptors at the cell surface and triggering signaling pathways that alter gene expression and other phenotypes in an autocrine or paracrine manner. Here, we show that the calcium-dependent cytokines S100A8 and S100A9 are recruited to numerous promoters and enhancers in a model of breast cellular transformation. This recruitment is associated with multiple DNA sequence motifs recognized by DNA binding transcription factors that are linked to transcriptional activation and are important for transformation. The cytokines interact with these transcription factors in nuclear extracts, and they activate transcription when artificially recruited to a target promoter. Nuclear-specific expression of S100A8/A9 promotes oncogenic transcription and leads to enhanced breast transformation phenotype. These results suggest that, in addition to its classical cytokine function, S100A8/A9 can act as a transcriptional coactivator.

Cellular transformation is associated with dramatic changes in gene expression, but it is difficult to determine which regulated genes are oncogenically relevant. Here we describe Pheno-RNA, a general approach to identifying candidate genes associated with a specific phenotype. Specifically, we generate a "phenotypic series" by treating a nontransformed breast cell line with a wide variety of molecules that induce cellular transformation to various extents. By performing transcriptional profiling across this phenotypic series, the expression profile of every gene can be correlated with the strength of the transformed phenotype. We identify ∼200 genes whose expression profiles are very highly correlated with the transformation phenotype, strongly suggesting their importance in transformation. Within biological categories linked to cancer, some genes show high correlations with the transformed phenotype, but others do not. Many genes whose expression profiles are highly correlated with transformation have never been associated with cancer, suggesting the involvement of heretofore unknown genes in cancer.

Yeast cells undergoing the diauxic response show a striking upstream shift in poly(A) site utilization, with increased use of ORF-proximal poly(A) sites resulting in shorter 3’ mRNA isoforms for most genes. This altered poly(A) pattern is extremely similar to that observed in cells containing Pol II derivatives with slow elongation rates. Conversely, cells containing derivatives with fast elongation rates show a subtle downstream shift in poly(A) sites. Polyadenylation patterns of many genes are sensitive to both fast and slow elongation rates, and a global shift of poly(A) utilization is strongly linked to increased purine content of sequences flanking poly(A) sites. Pol II processivity is impaired in diauxic cells, but strains with reduced processivity and normal Pol II elongation rates have normal polyadenylation profiles. Thus, Pol II elongation speed is important for poly(A) site selection and for regulating poly(A) patterns in response to environmental conditions.

Transient activation of Src oncoprotein in non-transformed, breast epithelial cells can initiate an epigenetic switch to the stably transformed state via a positive feedback loop that involves the inflammatory transcription factors STAT3 and NF-κB. Here, we develop an experimental and computational pipeline that includes 1) a Bayesian network model (AccessTF) that accurately predicts protein-bound DNA sequence motifs based on chromatin accessibility, and 2) a scoring system (TFScore) that rank-orders transcription factors as candidates for being important for a biological process. Genetic experiments validate TFScore and suggest that more than 40 transcription factors contribute to the oncogenic state in this model. Interestingly, individual depletion of several of these factors results in similar transcriptional profiles, indicating that a complex and interconnected transcriptional network promotes a stable oncogenic state. The combined experimental and computational pipeline represents a general approach to comprehensively identify transcriptional regulators important for a biological process., Systematic analysis of the control of dynamic cellular processes remains a challenge. Here the authors introduce a pipeline enabling them to identify TFs involved in Src-induced cellular transformation, and find that a large number of TFs with diverse DNA binding specificities orchestrate the process.

Transcription factor binding to target sites in vivo is a dynamic process that involves cycles of association and dissociation, with individual proteins differing in their binding dynamics. The dynamics at individual sites on a genomic scale has been investigated in yeast cells, but comparable experiments have not been done in multicellular eukaryotes. Here, we describe a tamoxifen-inducible, time-course ChIP-seq approach to measure transcription factor binding dynamics at target sites throughout the human genome. As observed in yeast cells, the TATA-binding protein (TBP) typically displays rapid turnover at RNA polymerase (Pol) II-transcribed promoters, slow turnover at Pol III promoters, and very slow turnover at the Pol I promoter. Interestingly, turnover rates vary widely among Pol II promoters in a manner that does not correlate with the level of TBP occupancy. Human Pol II promoters with slow TBP dissociation preferentially contain a TATA consensus motif, support high transcriptional activity of downstream genes, and are linked with specific activators and chromatin remodelers. These properties of human promoters with slow TBP turnover differ from those of yeast promoters with slow turnover. These observations suggest that TBP binding dynamics differentially affect promoter function and gene expression, possibly at the level of transcriptional reinitiation/bursting.

Using an inducible, inflammatory model of breast cellular transformation, we describe the transcriptional regulatory network mediated by STAT3, NF-κB, and AP-1 factors on a genomic scale. These proinflammatory regulators form transcriptional complexes that directly regulate the expression of hundreds of genes in oncogenic pathways via a positive feedback loop. This transcriptional feedback loop and associated network functions to various extents in many types of cancer cells and patient tumors, and it is the basis for a cancer inflammation index that defines cancer types by functional criteria. We identify a network of noninflammatory genes whose expression is well correlated with the cancer inflammatory index. Conversely, the cancer inflammation index is negatively correlated with the expression of genes involved in DNA metabolism, and transformation is associated with genome instability. We identify drugs whose efficacy in cell lines is correlated with the cancer inflammation index, suggesting the possibility of using this index for personalized cancer therapy. Inflammatory tumors are preferentially associated with infiltrating immune cells that might be recruited to the site of the tumor via inflammatory molecules produced by the cancer cells.

Transcription by RNA polymerase II requires assembly of a preinitiation complex (PIC) composed of general transcription factors (GTFs) bound at the promoter. In vitro, some GTFs are essential for transcription, whereas others are not required under certain conditions. PICs are stable in the absence of nucleotide triphosphates, and subsets of GTFs can form partial PICs. By depleting individual GTFs in yeast cells, we show that all GTFs are essential for TBP binding and transcription, suggesting that partial PICs do not exist at appreciable levels in vivo. Depletion of FACT, a histone chaperone that travels with elongating Pol II, strongly reduces PIC formation and transcription. In contrast, TBP-associated factors (TAFs) contribute to transcription of most genes, but TAF-independent transcription occurs at substantial levels, preferentially at promoters containing TATA elements. PICs are absent in cells deprived of uracil, and presumably UTP, suggesting that transcriptionally inactive PICs are removed from promoters in vivo.

Mediator is a transcriptional co-activator recruited to enhancers by DNA-binding activators, and it also interacts with RNA polymerase (Pol) II as part of the preinitiation complex (PIC). We demonstrate that a single Mediator complex associates with the enhancer and core promoter in vivo, indicating that it can physically bridge these transcriptional elements. However, the Mediator kinase module associates strongly with the enhancer, but not with the core promoter, and it dissociates from the enhancer upon depletion of the TFIIH kinase. Severing the kinase module from Mediator by removing the connecting subunit Med13 does not affect Mediator association at the core promoter, but increases occupancy at enhancers. Thus, Mediator undergoes a compositional change in which the kinase module, recruited via Mediator to the enhancer, dissociates from Mediator to permit association with Pol II and the PIC. As such, Mediator acts as a dynamic bridge between the enhancer and core promoter.

// Whitney S. Henry 1,* , David G. Hendrickson 2,3,* , Francisco Beca 1 , Benjamin Glass 1 , Marianne Lindahl-Allen 4 , Lizhi He 4 , Zhe Ji 4 , Kevin Struhl 4 , Andrew H. Beck 1 , John L. Rinn 2,3 and Alex Toker 1 1 Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA 2 Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA 3 Broad Institute of MIT and Harvard, Cambridge, MA, USA 4 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA * These authors have contributed equally to this work Correspondence to: Alex Toker, email: // John L. Rinn, email: // Keywords : lncRNA, Src, PI3K, breast cancer, LINC00520 Received : March 29, 2016 Accepted : September 02, 2016 Published : September 10, 2016 Abstract Long non-coding RNAs (lncRNAs) have been implicated in normal cellular homeostasis as well as pathophysiological conditions, including cancer. Here we performed global gene expression profiling of mammary epithelial cells transformed by oncogenic v-Src , and identified a large subset of uncharacterized lncRNAs potentially involved in breast cancer development. Specifically, our analysis revealed a novel lncRNA, LINC00520 that is upregulated upon ectopic expression of oncogenic v-Src , in a manner that is dependent on the transcription factor STAT3. Similarly, LINC00520 is also increased in mammary epithelial cells transformed by oncogenic PI3K and its expression is decreased upon knockdown of mutant PIK3CA. Additional expression profiling highlight that LINC00520 is elevated in a subset of human breast carcinomas, with preferential enrichment in the basal-like molecular subtype. ShRNA-mediated depletion of LINC00520 results in decreased cell migration and loss of invasive structures in 3D. RNA sequencing analysis uncovers several genes that are differentially expressed upon ectopic expression of LINC00520, a significant subset of which are also induced in v-Src -transformed MCF10A cells. Together, these findings characterize LINC00520 as a lncRNA that is regulated by oncogenic Src, PIK3CA and STAT3, and which may contribute to the molecular etiology of breast cancer.

Ribosome profiling is widely used to study translation in vivo, but not all sequence reads correspond to ribosome-protected RNA. Here, we develop Rfoot, a computational pipeline that analyzes ribosomal profiling data and identifies native, non-ribosomal RNA-protein complexes in the same sample.. We use Rfoot to precisely map RNase-protected regions within small nucleolar RNAs, spliceosomal RNAs, microRNAs, tRNAs, long noncoding (lnc) RNAs, and 3’ˊ untranslated regions of mRNAs in human cells. We show that RNAs of the same class can show differential complex association. Although only a subset of lncRNAs show RNase footprints, many of these have multiple footprints, and the protected regions are evolutionarily conserved, suggestive of biological functions.

Summary Alternative polyadenylation generates numerous 3′ mRNA isoforms that can vary in biological properties, such as stability and localization. We developed methods to obtain transcriptome-scale structural information and protein binding on individual 3′ mRNA isoforms in vivo. Strikingly, near-identical mRNA isoforms can possess dramatically different structures throughout the 3′ UTR. Analyses of identical mRNAs in different species or refolded in vitro indicate that structural differences in vivo are often due to trans-acting factors. The level of Pab1 binding to poly(A)-containing isoforms is surprisingly variable, and differences in Pab1 binding correlate with the extent of structural variation for closely spaced isoforms. A pattern encompassing single-strandedness near the 3′ terminus, double-strandedness of the poly(A) tail, and low Pab1 binding is associated with mRNA stability. Thus, individual 3′ mRNA isoforms can be remarkably different physical entities in vivo. Sequences responsible for isoform-specific structures, differential Pab1 binding, and mRNA stability are evolutionarily conserved, indicating biological function.

The PI3K-Akt-mTOR signaling pathway is a master regulator of RNA translation. Pharmacological inhibition of this pathway preferentially and coordinately suppresses, in a 4EBP1/2-dependent manner, translation of mRNAs encoding ribosomal proteins. However, it is unclear whether mechanistic target of rapamycin (mTOR)-4EBP1/2 is the exclusive translation regulator of this group of genes, and furthermore, systematic searches for novel translation modulators have been immensely challenging because of difficulties in scaling existing RNA translation profiling assays. Here, we developed a rapid and highly scalable approach for gene-specific quantitation of RNA translation, termed Targeted Profiling of RNA Translation (TPRT). We applied this technique in a chemical screen for translation modulators, and identified numerous preclinical and clinical therapeutic compounds, with diverse nominal targets, that preferentially suppress translation of ribosomal proteins. Surprisingly, some of these compounds act in a manner that bypasses canonical regulation by mTOR-4EBP1/2. Instead, these compounds exert their translation effects in a manner that is dependent on GCN2-eIF2α, a central signaling axis within the integrated stress response. Furthermore, we were also able to identify metabolic perturbations that also suppress ribosomal protein translation in an mTOR-independent manner. Together, we describe a translation assay that is directly applicable to large-scale RNA translation studies, and that enabled us to identify a noncanonical, mTOR-independent mode for translation regulation of ribosomal proteins.

Colony formation in soft agar is the gold-standard assay for cellular transformation in vitro, but it is unsuited for high-throughput screening. Here, we describe an assay for cellular transformation that involves growth in low attachment (GILA) conditions and is strongly correlated with the soft-agar assay. Using GILA, we describe high-throughput screens for drugs and genes that selectively inhibit or increase transformation, but not proliferation. Such molecules are unlikely to be found through conventional drug screening, and they include kinase inhibitors and drugs for noncancer diseases. In addition to known oncogenes, the genetic screen identifies genes that contribute to cellular transformation. Lastly, we demonstrate the ability of Food and Drug Administration-approved noncancer drugs to selectively kill ovarian cancer cells derived from patients with chemotherapy-resistant disease, suggesting this approach may provide useful information for personalized cancer treatment.

Summary: Nutrient deprivation inhibits mRNA translation through mTOR and eIF2α signaling, but it is unclear how the translational program is controlled to reflect the degree of a metabolic stress. In a model of breast cellular transformation, various forms of nutrient deprivation differentially affect the rate of protein synthesis and its recovery over time. Genome-wide translational profiling of glutamine-deprived cells reveals a rapid upregulation of mRNAs containing uORFs and downregulation of ribosomal protein mRNAs, which are followed by selective translation of cytokine and inflammatory mRNAs. Transcription and translation of inflammatory and cytokine genes are stimulated in response to diverse metabolic stresses and depend on eIF2α phosphorylation, with the extent of stimulation correlating with the decrease in global protein synthesis. In accord with the inflammatory stimulus, glutamine deprivation stimulates the migration of transformed cells. Thus, pro-inflammatory gene expression is coupled to metabolic stress, and this can affect cancer cell behavior upon nutrient limitation. : Deprivation of some nutrients may impose more constraints on mRNA translation than others. Gameiro and Struhl describe a relationship between translational repression and pro-inflammatory gene expression in response to various metabolic stresses. The pro-inflammatory transcriptional and translational response is not triggered by mTOR inhibition, per se, and requires eIF2α phosphorylation. Keywords: inflammation, glutamine, metabolism, translational control, protein synthesis, cancer, mTOR, eIF2α

The Mediator complex has been described as a general transcription factor, but it is unclear if it is essential for Pol II transcription and/or is a required component of the preinitiation complex (PIC) in vivo. Here, we show that depletion of individual subunits, even those essential for cell growth, causes a general but only modest decrease in transcription. In contrast, simultaneous depletion of all Mediator modules causes a drastic decrease in transcription. Depletion of head or middle subunits, but not tail subunits, causes a downstream shift in the Pol II occupancy profile, suggesting that Mediator at the core promoter inhibits promoter escape. Interestingly, a functional PIC and Pol II transcription can occur when Mediator is not detected at core promoters. These results provide strong evidence that Mediator is essential for Pol II transcription and stimulates PIC formation, but it is not a required component of the PIC in vivo. DOI: http://dx.doi.org/10.7554/eLife.28447.001