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

2010-2015 North American methane emissions, sectoral contributions, and trends: A high-resolution inversion of GOSAT observations of atmospheric methane

We use 2010 2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0:5-0:625 resolution in concentrated source regions. The analytical solution provides a closedform characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTs model ensemble for wetlands. Our best estimate for mean 2010 2015 US anthropogenic emissions is 30.6 (range: 29.4 31.3) Tg a-1, slightly higher than the gridded EPA inventory (28.7 (26.4 36.2) Tg a-1). The main discrepancy is for the oil and gas production sectors, where we find higher emissions than the GHGI by 35% and 22 %, respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production, and we find that these are lower than our estimate by a factor of 2. Our best estimate of US wetland emissions is 10.2 (5.6 11.1) Tg a-1, on the low end of the prior WetCHARTs inventory uncertainty range (14.2 (3.3 32.4) Tg a-1), which calls for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010 2015 of 0.4%a-1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil and gas production in the eastern US. We also find that oil and gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010 2015 decrease in emissions from offshore oil production

Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage

Isogenic cells show a large degree of variability in growth rate, even when cultured in the same environment. Such cell-to-cell variability in growth can alter sensitivity to antibiotics, chemotherapy and environmental stress. To characterize transcriptional differences associated with this variability, we have developed a method--FitFlow--that enables the sorting of subpopulations by growth rate. The slow-growing subpopulation shows a transcriptional stress response, but, more surprisingly, these cells have reduced RNA polymerase fidelity and exhibit a DNA damage response. As DNA damage is often caused by oxidative stress, we test the addition of an antioxidant, and find that it reduces the size of the slow-growing population. More generally, we find a significantly altered transcriptome in the slow-growing subpopulation that only partially resembles that of cells growing slowly due to environmental and culture conditions. Slow-growing cells upregulate transposons and express more chromosomal, viral and plasmid-borne transcripts, and thus explore a larger genotypic--and so phenotypic--space.This work was supported by grants to B.L. from the European Research Council Consolidator Grant (IR-DC 616434), Spanish Ministry of Economy and Competitiveness (BFU2011-26206), the AXA Research Fund, Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), and the EMBL-CRG Systems Biology Program. This work was supported by grants to L.B.C. from the department and the AGAUR program (2014 SGR 0974). R.D. acknowledges support from Swiss National Science Foundation through Early Postdoc Mobility Fellowship. D.v.D. was supported by an NWO Rubicon fellowship (825.14.016)

Allergen Immunotherapy Adherence in the Real World: How Bad Is It and How Can It Be Improved?

adherence in allergic disease