Decrypting the programming of β-methylation in virginiamycin M biosynthesis

During biosynthesis by multi-modular trans-AT polyketide synthases, polyketide structural space can be expanded by conversion of initially-formed electrophilic ß-ketones into ß-alkyl groups. These multi-step transformations are catalysed by 3-hydroxy-3-methylgluratryl synthase cassettes of enzymes. While mechanistic aspects of these reactions have been delineated, little information is available concerning how the cassettes select the specific polyketide intermediate(s) to target. Here we use integrative structural biology to identify the basis for substrate choice in module 5 of the virginiamycin M trans-AT polyketide synthase. Additionally, we show in vitro that module 7, at minimum, is a potential additional site for ß-methylation. Indeed, analysis by HPLC-MS coupled with isotopic labelling and pathway inactivation identifies a metabolite bearing a second ß-methyl at the expected position. Collectively, our results demonstrate that several control mechanisms acting in concert underpin ß-branching programming. Furthermore, variations in this control – whether natural or by design – open up avenues for diversifying polyketide structures towards high-value derivatives

Compte-rendu de la journée d’étude « Qualité de vie et cohésion au travail »

Les élèves conservatrices et conservateurs promus DCB30 proposent un retour sur la journée d’étude consacrée à la qualité de vie et cohésion au travail qu’ils ont organisée le 21 mai 2021. Cette journée s’est déclinée autour de deux conférences introductives sur le thème de la qualité de vie au travail, des ateliers pour permettre à de futurs cadres de réfléchir et de mettre en place des actions relatives à ces thèmes et enfin des retours d’expériences

PRALIMAP: study protocol for a high school-based, factorial cluster randomised interventional trial of three overweight and obesity prevention strategies

<p>Abstract</p> <p>Background</p> <p>Given the increase in overweight and obesity prevalence in adolescents in the last decade, effective prevention strategies for these conditions in adolescents are urgently needed. The PRALIMAP (Promotion de l'ALImentation et de l'Activité Physique) trial aims to evaluate the effectiveness for these conditions of 3 health promotion strategies -- educational, screening and environmental -- applied singly or in combination in high schools over a 2-year intervention period.</p> <p>Methods</p> <p>PRALIMAP is a stratified 2 × 2 × 2 factorial cluster randomised controlled trial including 24 state high schools in Lorraine, northeastern France, in 2 waves: 8 schools in 2006 (wave 1) and 16 in 2007 (wave 2). Students entering the selected high schools in the 4 academic years from 2006 to 2009 are eligible for data collection. Interventional strategies are organized over 2 academic years. The follow-up consists of 3 visits: at the entry of grade 10 (T0), grade 11 (T1) and grade 12 (T2). At T0, 5,458 (85.7%) adolescents participated. The educational strategy consists of nutritional lessons, working groups and a final party. The screening strategy consists in detecting overweight/obesity and eating disorders in adolescents and proposing, if necessary, an adapted care management program of 7 group educational sessions. The environmental strategy consists in improving dietary and physical activity offerings in high schools and facilities, especially catering. The main outcomes are body size evolution over time, nutritional behaviour and knowledge, health and quality of life. An evaluation process documents how each intervention strategy is implemented in the schools and estimates the dose of the intervention, allowing for a per protocol analysis after the main intention-to-treat analysis.</p> <p>Discussion</p> <p>PRALIMAP aims at improving the prevention and management of overweight and obesity in adolescents by translating current evidence into public health practice. Particular attention is paid to clustering, multiple factorials and long-term duration to address common pitfalls in health promotion trials. The results should inform how best to implement, in a school environment, effective nutrition prevention programs targeting adolescents who are at a point their lives when they develop responsibilities and empowerment for health attitude behaviours.</p> <p>Trial registration</p> <p>This trial is registered at ClinicalTrials.gov under <a href="http://clinicaltrials.gov/ct2/show/NCT00814554">NCT00814554</a>.</p

Longevity biotechnology:bridging AI, biomarkers, geroscience and clinical applications for healthy longevity

The recent unprecedented progress in ageing research and drug discovery brings together fundamental research and clinical applications to advance the goal of promoting healthy longevity in the human population. We, from the gathering at the Aging Research and Drug Discovery Meeting in 2023, summarised the latest developments in healthspan biotechnology, with a particular emphasis on artificial intelligence (AI), biomarkers and clocks, geroscience, and clinical trials and interventions for healthy longevity. Moreover, we provide an overview of academic research and the biotech industry focused on targeting ageing as the root of age-related diseases to combat multimorbidity and extend healthspan. We propose that the integration of generative AI, cutting-edge biological technology, and longevity medicine is essential for extending the productive and healthy human lifespan

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

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Multi-messenger Observations of a Binary Neutron Star Merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 {{s}} with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of {40}-8+8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 {M}ȯ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 {{Mpc}}) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.</p