Mutations of RNA polymerase II activate key genes of the nucleoside triphosphate biosynthetic pathways

The yeast URA2 gene, encoding the rate-limiting enzyme of UTP biosynthesis, is transcriptionally activated by UTP shortage. In contrast to other genes of the UTP pathway, this activation is not governed by the Ppr1 activator. Moreover, it is not due to an increased recruitment of RNA polymerase II at the URA2 promoter, but to its much more effective progression beyond the URA2 mRNA start site(s). Regulatory mutants constitutively expressing URA2 resulted from cis-acting deletions upstream of the transcription initiator region, or from amino-acid replacements altering the RNA polymerase II Switch 1 loop domain, such as rpb1-L1397S. These two mutation classes allowed RNA polymerase to progress downstream of the URA2 mRNA start site(s). rpb1-L1397S had similar effects on IMD2 (IMP dehydrogenase) and URA8 (CTP synthase), and thus specifically activated the rate-limiting steps of UTP, GTP and CTP biosynthesis. These data suggest that the Switch 1 loop of RNA polymerase II, located at the downstream end of the transcription bubble, may operate as a specific sensor of the nucleoside triphosphates available for transcription

Mobile HIV Screening in Cape Town, South Africa: Clinical Impact, Cost and Cost-Effectiveness

Background: Mobile HIV screening may facilitate early HIV diagnosis. Our objective was to examine the cost-effectiveness of adding a mobile screening unit to current medical facility-based HIV testing in Cape Town, South Africa. Methods and Findings: We used the Cost Effectiveness of Preventing AIDS Complications International (CEPAC-I) computer simulation model to evaluate two HIV screening strategies in Cape Town: 1) medical facility-based testing (the current standard of care) and 2) addition of a mobile HIV-testing unit intervention in the same community. Baseline input parameters were derived from a Cape Town-based mobile unit that tested 18,870 individuals over 2 years: prevalence of previously undiagnosed HIV (6.6%), mean CD4 count at diagnosis (males 423/µL, females 516/µL), CD4 count-dependent linkage to care rates (males 31%–58%, females 49%–58%), mobile unit intervention cost (includes acquisition, operation and HIV test costs, $29.30 per negative result and$31.30 per positive result). We conducted extensive sensitivity analyses to evaluate input uncertainty. Model outcomes included site of HIV diagnosis, life expectancy, medical costs, and the incremental cost-effectiveness ratio (ICER) of the intervention compared to medical facility-based testing. We considered the intervention to be “very cost-effective” when the ICER was less than South Africa's annual per capita Gross Domestic Product (GDP) ($8,200 in 2012). We projected that, with medical facility-based testing, the discounted (undiscounted) HIV-infected population life expectancy was 132.2 (197.7) months; this increased to 140.7 (211.7) months with the addition of the mobile unit. The ICER for the mobile unit was$2,400/year of life saved (YLS). Results were most sensitive to the previously undiagnosed HIV prevalence, linkage to care rates, and frequency of HIV testing at medical facilities. Conclusion: The addition of mobile HIV screening to current testing programs can improve survival and be very cost-effective in South Africa and other resource-limited settings, and should be a priority

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

<p>Abstract</p> <p>Background</p> <p>The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.</p> <p>Results</p> <p>We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality.</p> <p>Conclusion</p> <p>We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality.</p

Rpb1 Sumoylation in Response to UV Radiation or Transcriptional Impairment in Yeast

Covalent modifications of proteins by ubiquitin and the Small Ubiquitin-like MOdifier (SUMO) have been revealed to be involved in a plethora of cellular processes, including transcription, DNA repair and DNA damage responses. It has been well known that in response to DNA damage that blocks transcription elongation, Rpb1, the largest subunit of RNA polymerase II (Pol II), is ubiquitylated and subsequently degraded in mammalian and yeast cells. However, it is still an enigma regarding how Pol II responds to damaged DNA and conveys signal(s) for DNA damage-related cellular processes. We found that Rpb1 is also sumoylated in yeast cells upon UV radiation or impairment of transcription elongation, and this modification is independent of DNA damage checkpoint activation. Ubc9, an E2 SUMO conjugase, and Siz1, an E3 SUMO ligase, play important roles in Rpb1 sumoylation. K1487, which is located in the acidic linker region between the C-terminal domain and the globular domain of Rpb1, is the major sumoylation site. Rpb1 sumoylation is not affected by its ubiquitylation, and vice versa, indicating that the two processes do not crosstalk. Abolishment of Rpb1 sumoylation at K1487 does not affect transcription elongation or transcription coupled repair (TCR) of UV-induced DNA damage. However, deficiency in TCR enhances UV-induced Rpb1 sumoylation, presumably due to the persistence of transcription-blocking DNA lesions in the transcribed strand of a gene. Remarkably, abolishment of Rpb1 sumoylation at K1487 causes enhanced and prolonged UV-induced phosphorylation of Rad53, especially in TCR-deficient cells, suggesting that the sumoylation plays a role in restraining the DNA damage checkpoint response caused by transcription-blocking lesions. Our results demonstrate a novel covalent modification of Rpb1 in response to UV induced DNA damage or transcriptional impairment, and unravel an important link between the modification and the DNA damage checkpoint response

Dissection of Pol II Trigger Loop Function and Pol II Activity–Dependent Control of Start Site Selection In Vivo

Structural and biochemical studies have revealed the importance of a conserved, mobile domain of RNA Polymerase II (Pol II), the Trigger Loop (TL), in substrate selection and catalysis. The relative contributions of different residues within the TL to Pol II function and how Pol II activity defects correlate with gene expression alteration in vivo are unknown. Using Saccharomyces cerevisiae Pol II as a model, we uncover complex genetic relationships between mutated TL residues by combinatorial analysis of multiply substituted TL variants. We show that in vitro biochemical activity is highly predictive of in vivo transcription phenotypes, suggesting direct relationships between phenotypes and Pol II activity. Interestingly, while multiple TL residues function together to promote proper transcription, individual residues can be separated into distinct functional classes likely relevant to the TL mechanism. In vivo, Pol II activity defects disrupt regulation of the GTP-sensitive IMD2 gene, explaining sensitivities to GTP-production inhibitors, but contrasting with commonly cited models for this sensitivity in the literature. Our data provide support for an existing model whereby Pol II transcriptional activity provides a proxy for direct sensing of NTP levels in vivo leading to IMD2 activation. Finally, we connect Pol II activity to transcription start site selection in vivo, implicating the Pol II active site and transcription itself as a driver for start site scanning, contravening current models for this process