The Cysteine Rich Necrotrophic Effector SnTox1 Produced by Stagonospora nodorum Triggers Susceptibility of Wheat Lines Harboring Snn1

The wheat pathogen Stagonospora nodorum produces multiple necrotrophic effectors (also called host-selective toxins) that promote disease by interacting with corresponding host sensitivity gene products. SnTox1 was the first necrotrophic effector identified in S. nodorum, and was shown to induce necrosis on wheat lines carrying Snn1. Here, we report the molecular cloning and validation of SnTox1 as well as the preliminary characterization of the mechanism underlying the SnTox1-Snn1 interaction which leads to susceptibility. SnTox1 was identified using bioinformatics tools and verified by heterologous expression in Pichia pastoris. SnTox1 encodes a 117 amino acid protein with the first 17 amino acids predicted as a signal peptide, and strikingly, the mature protein contains 16 cysteine residues, a common feature for some avirulence effectors. The transformation of SnTox1 into an avirulent S. nodorum isolate was sufficient to make the strain pathogenic. Additionally, the deletion of SnTox1 in virulent isolates rendered the SnTox1 mutated strains avirulent on the Snn1 differential wheat line. SnTox1 was present in 85% of a global collection of S. nodorum isolates. We identified a total of 11 protein isoforms and found evidence for strong diversifying selection operating on SnTox1. The SnTox1-Snn1 interaction results in an oxidative burst, DNA laddering, and pathogenesis related (PR) gene expression, all hallmarks of a defense response. In the absence of light, the development of SnTox1-induced necrosis and disease symptoms were completely blocked. By comparing the infection processes of a GFP-tagged avirulent isolate and the same isolate transformed with SnTox1, we conclude that SnTox1 may play a critical role during fungal penetration. This research further demonstrates that necrotrophic fungal pathogens utilize small effector proteins to exploit plant resistance pathways for their colonization, which provides important insights into the molecular basis of the wheat-S. nodorum interaction, an emerging model for necrotrophic pathosystems

Global prevalence and genotype distribution of hepatitis C virus infection in 2015 : A modelling study

Publisher Copyright: © 2017 Elsevier LtdBackground The 69th World Health Assembly approved the Global Health Sector Strategy to eliminate hepatitis C virus (HCV) infection by 2030, which can become a reality with the recent launch of direct acting antiviral therapies. Reliable disease burden estimates are required for national strategies. This analysis estimates the global prevalence of viraemic HCV at the end of 2015, an update of—and expansion on—the 2014 analysis, which reported 80 million (95% CI 64–103) viraemic infections in 2013. Methods We developed country-level disease burden models following a systematic review of HCV prevalence (number of studies, n=6754) and genotype (n=11 342) studies published after 2013. A Delphi process was used to gain country expert consensus and validate inputs. Published estimates alone were used for countries where expert panel meetings could not be scheduled. Global prevalence was estimated using regional averages for countries without data. Findings Models were built for 100 countries, 59 of which were approved by country experts, with the remaining 41 estimated using published data alone. The remaining countries had insufficient data to create a model. The global prevalence of viraemic HCV is estimated to be 1·0% (95% uncertainty interval 0·8–1·1) in 2015, corresponding to 71·1 million (62·5–79·4) viraemic infections. Genotypes 1 and 3 were the most common cause of infections (44% and 25%, respectively). Interpretation The global estimate of viraemic infections is lower than previous estimates, largely due to more recent (lower) prevalence estimates in Africa. Additionally, increased mortality due to liver-related causes and an ageing population may have contributed to a reduction in infections. Funding John C Martin Foundation.publishersversionPeer reviewe

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world's forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO₂ fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO₂ fertilization effects may considerably reduce the range of projections.United States. Environmental Protection Agency (DW-012-92388301)United States. Environmental Protection Agency (XA-83600001)United States. Department of Energy (DEFG02-94ER61937