Unlike normal mammalian cells, which use oxygen to generate energy, cancer cells rely on glycolysis for energy and are therefore less dependent on oxygen. We previously observed that the c-Myc oncogenic transcription factor regulates lactate dehydrogenase A and induces lactate overproduction. We, therefore, sought to determine whether c-Myc controls other genes regulating glucose metabolism. In Rat1a fibroblasts and murine livers overexpressing c-Myc, the mRNA levels of the glucose transporter GLUT1, phosphoglucose isomerase, phosphofructokinase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, and enolase were elevated. c-Myc directly transactivates genes encoding GLUT1, phosphofructokinase, and enolase and increases glucose uptake in Rat1 fibroblasts. Nuclear run-on studies confirmed that the GLUT1 transcriptional rate is elevated by c-Myc. Our findings suggest that overexpression of the c-Myc oncoprotein deregulates glycolysis through the activation of several components of the glucose metabolic pathway.To form a three-dimensional multicellular spheroid mass, neoplastic cells alter their metabolism such that they are able to survive and grow in the hostile microenvironments created by the decreased blood flow found in tumor vasculature (1, 2). The most striking feature of tumor cells is the production of large amounts of lactic acid, which is due to the glycolytic conversion of glucose to lactic acid even in the presence of oxygen (3). This is often accompanied by an increased rate of glucose transport (4 -6).Glucose is a major regulator of gene transcription. In particular, it stimulates transcription of genes encoding glycolytic and lipogenic enzymes in adipocytes and hepatocytes through the carbohydrate response element (ChoRE), 1 a 5Ј-CACGTG-3Ј motif (7-11). The ChoRE is similar to the core binding site for the transcription factors USF2 (12), which is implicated in glucose metabolism, TFE3, and the hypoxia-inducible transcription factor (HIF). Hence, the ChoRE serves to integrate physiological signals through transcription factors to regulate glucose metabolism.During tumor formation, adaptation to hypoxia may be mediated by the HIF-1 family of transcription factors, which induce angiogenesis and other metabolic changes. (2,13,14). It is notable that glucose transport and transporter mRNA are induced in cells transformed by ras or src oncogenes (5). The c-myc oncogene is activated in a variety of pathways that are important in controlling cell growth and tumorigenesis. (15-18). Intriguingly, the ChoRE sequence matches the core E-box (5Ј-CACGTG-3Ј) binding site for c-Myc, which binds E-boxes of target genes to stimulate transcription (2,18,19). Previous work showed that c-Myc directly up-regulates the expression of the lactate dehydrogenase gene (LDH-A) (20), which is important in the transformed phenotype (anchorage-independent growth) of cells that overexpress c-Myc (20, 21). In addition to LDH-A, we report here the deregulation of GLUT 1 and several glycolytic genes by c-Myc. EXPERIME...

High-throughput methods, such as ribosome profiling, have revealed the complexity of translation regulation in Bacteria and Eukarya with large-scale effects on cellular functions. In contrast, the translational landscape in Archaea remains mostly unexplored. Here, we developed ribosome profiling in a model archaeon, Haloferax volcanii, elucidating, for the first time, the translational landscape of a representative of the third domain of life. We determined the ribosome footprint of H. volcanii to be comparable in size to that of the Eukarya. We linked footprint lengths to initiating and elongating states of the ribosome on leadered transcripts, operons, and on leaderless transcripts, the latter representing 70% of H. volcanii transcriptome. We manipulated ribosome activity with translation inhibitors to reveal ribosome pausing at specific codons. Lastly, we found that the drug harringtonine arrested ribosomes at initiation sites in this archaeon. This drug treatment allowed us to confirm known translation initiation sites and also reveal putative novel initiation sites in intergenic regions and within genes. Ribosome profiling revealed an uncharacterized complexity of translation in this archaeon with bacteria-like, eukarya-like, and potentially novel translation mechanisms. These mechanisms are likely to be functionally essential and to contribute to an expanded proteome with regulatory roles in gene expression.

Regulatory small RNAs (sRNAs) play large-scale and essential roles in many cellular processes across all domains of life. Microbial sRNAs have been extensively studied in model organisms, but very little is known about the dynamics of sRNA synthesis and their roles in the natural environment. In this study, we discovered hundreds of intergenic (itsRNAs) and antisense (asRNAs) sRNAs expressed in an extremophilic microbial community inhabiting halite nodules (salt rocks) in the Atacama Desert. For this, we built SnapT, a new sRNA annotation pipeline that can be applied to any microbial community. We found asRNAs with expression levels negatively correlated with that of their overlapping putative target and itsRNAs that were conserved and significantly differentially expressed between 2 sampling time points. We demonstrated that we could perform target prediction and correlate expression levels between sRNAs and predicted target mRNAs at the community level. Functions of putative mRNA targets reflected the environmental challenges members of the halite communities were subjected to, including osmotic adjustments to a major rain event and competition for nutrients. IMPORTANCE Microorganisms in the natural world are found in communities, communicating and interacting with each other; therefore, it is essential that microbial regulatory mechanisms, such as gene regulation affected by small RNAs (sRNAs), be investigated at the community level. This work demonstrates that metatranscriptomic field experiments can link environmental variation with changes in RNA pools and have the potential to provide new insights into environmental sensing and responses in natural microbial communities through noncoding RNA-mediated gene regulation.

While haloarchaea are highly resistant to oxidative stress, a comprehensive understanding of the processes regulating this remarkable response is lacking. Oxidative stress-responsive small non-coding RNAs (sRNAs) have been reported in the model archaeon, Haloferax volcanii, but targets and mechanisms have not been elucidated. Using a combination of high throughput and reverse molecular genetic approaches, we elucidated the functional role of the most up-regulated intergenic sRNA during oxidative stress in H. volcanii, named Small RNA in Haloferax Oxidative Stress (SHOxi). SHOxi was predicted to form a stable secondary structure with a conserved stem-loop region as the potential binding site for trans-targets. NAD-dependent malic enzyme mRNA, identified as a putative target of SHOxi, interacted directly with a putative 'seed' region within the predicted stem loop of SHOxi. Malic enzyme catalyzes the oxidative decarboxylation of malate into pyruvate using NAD + as a cofactor. The destabilization of malic enzyme mRNA, and the decrease in the NAD + /NADH ratio, resulting from the direct RNA-RNA interaction between SHOxi and its trans-target was essential for the survival of H. volcanii to oxidative stress. These findings indicate that SHOxi likely regulates redox homoeostasis during oxidative stress by the post-transcriptional destabilization of malic enzyme mRNA. SHOximediated regulation provides evidence that the fine-tuning of metabolic cofactors could be a core strategy to mitigate damage from oxidative stress and confer resistance. This study is the first to establish the regulatory effects of sRNAs on mRNAs during the oxidative stress response in Archaea.

As RNA-Seq and other high-throughput sequencing grow in use and remain critical for gene expression studies, technical variability in counts data impedes studies of differential expression studies, data across samples and experiments, or reproducing results. Studies like Dillies et al. (2013) compare several between-lane normalization methods involving scaling factors, while Hansen et al. (2012) and Risso et al. (2014) propose methods that correct for sample-specific bias or use sets of control genes to isolate and remove technical variability. This paper evaluates four normalization methods in terms of reducing intra-group, technical variability and facilitating differential expression analysis or other research where the biological, inter-group variability is of interest. To this end, the four methods were evaluated in differential expression analysis between data from Pickrell et al. (2010) and Montgomery et al. (2010) and between simulated data modeled on these two datasets. Though the between-lane scaling factor methods perform worse on real data sets, they are much stronger for simulated data. We cannot reject the recommendation of Dillies et al. to use TMM and DESeq normalization, but further study of power to detect effects of different size under each normalization method is merited.