Genome-wide transcriptional profiling of appressorium development by the rice blast fungus Magnaporthe oryzae.

addresses: College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom.notes: PMCID: PMC3276559The rice blast fungus Magnaporthe oryzae is one of the most significant pathogens affecting global food security. To cause rice blast disease the fungus elaborates a specialised infection structure called an appressorium. Here, we report genome wide transcriptional profile analysis of appressorium development using next generation sequencing (NGS). We performed both RNA-Seq and High-Throughput SuperSAGE analysis to compare the utility of these procedures for identifying differential gene expression in M. oryzae. We then analysed global patterns of gene expression during appressorium development. We show evidence for large-scale gene expression changes, highlighting the role of autophagy, lipid metabolism and melanin biosynthesis in appressorium differentiation. We reveal the role of the Pmk1 MAP kinase as a key global regulator of appressorium-associated gene expression. We also provide evidence for differential expression of transporter-encoding gene families and specific high level expression of genes involved in quinate uptake and utilization, consistent with pathogen-mediated perturbation of host metabolism during plant infection. When considered together, these data provide a comprehensive high-resolution analysis of gene expression changes associated with cellular differentiation that will provide a key resource for understanding the biology of rice blast disease

mTOR: from growth signal integration to cancer, diabetes and ageing

In all eukaryotes, the target of rapamycin (TOR) signalling pathway couples energy and nutrient abundance to the execution of cell growth and division, owing to the ability of TOR protein kinase to simultaneously sense energy, nutrients and stress and, in metazoans, growth factors. Mammalian TOR complex 1 (mTORC1) and mTORC2 exert their actions by regulating other important kinases, such as S6 kinase (S6K) and Akt. In the past few years, a significant advance in our understanding of the regulation and functions of mTOR has revealed the crucial involvement of this signalling pathway in the onset and progression of diabetes, cancer and ageing.National Institutes of Health (U.S.)Howard Hughes Medical InstituteWhitehead Institute for Biomedical ResearchJane Coffin Childs Memorial Fund for Medical Research (Postdoctoral Fellowship)Human Frontier Science Program (Strasbourg, France

Determinants of the urinary and serum metabolome in children from six European populations

Background Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project (http://www.projecthelix.eu). Methods Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput 1H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates AbsoluteIDQ p180 kit). Results We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated (r > 0.18) between urine and serum. Conclusions We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Rheb Is Essential for Murine Development ▿

Ras homolog enriched in brain (Rheb) couples growth factor signaling to activation of the target of rapamycin complex 1 (TORC1). To study its role in mammals, we generated a Rheb knockout mouse. In contrast to mTOR or regulatory-associated protein of mTOR (Raptor) mutants, the inner cell mass of Rheb−/− embryos differentiated normally. Nevertheless, Rheb−/− embryos died around midgestation, most likely due to impaired development of the cardiovascular system. Rheb−/− embryonic fibroblasts showed decreased TORC1 activity, were smaller, and showed impaired proliferation. Rheb heterozygosity extended the life span of tuberous sclerosis complex 1-deficient (Tsc1−/−) embryos, indicating that there is a genetic interaction between the Tsc1 and Rheb genes in mouse

mTOR Inhibition Induces Compensatory, Therapeutically Targetable MEK Activation in Renal Cell Carcinoma

<div><p>Rapamycin derivatives allosterically targeting mTOR are currently FDA approved to treat advanced renal cell carcinoma (RCC), and catalytic inhibitors of mTOR/PI3K are now in clinical trials for treating various solid tumors. We sought to investigate the relative efficacy of allosteric versus catalytic mTOR inhibition, evaluate the crosstalk between the mTOR and MEK/ERK pathways, as well as the therapeutic potential of dual mTOR and MEK inhibition in RCC. Pharmacologic (rapamycin and BEZ235) and genetic manipulation of the mTOR pathway were evaluated by <i>in vitro</i> assays as monotherapy as well as in combination with MEK inhibition (GSK1120212). Catalytic mTOR inhibition with BEZ235 decreased proliferation and increased apoptosis better than allosteric mTOR inhibition with rapamycin. While mTOR inhibition upregulated MEK/ERK signaling, concurrent inhibition of both pathways had enhanced therapeutic efficacy. Finally, primary RCC tumors could be classified into subgroups [(I) MEK activated, (II) Dual MEK and mTOR activated, (III) Not activated, and (IV) mTOR activated] based on their relative activation of the PI3K/mTOR and MEK pathways. Patients with mTOR only activated tumors had the worst prognosis. In summary, dual targeting of the mTOR and MEK pathways in RCC can enhance therapeutic efficacy and primary RCC can be subclassified based on their relative levels of mTOR and MEK activation with potential therapeutic implications.</p></div