AsrR Is an Oxidative Stress Sensing Regulator Modulating Enterococcus faecium Opportunistic Traits, Antimicrobial Resistance, and Pathogenicity

Oxidative stress serves as an important host/environmental signal that triggers a wide range of responses in microorganisms. Here, we identified an oxidative stress sensor and response regulator in the important multidrug-resistant nosocomial pathogen Enterococcus faecium belonging to the MarR family and called AsrR (antibiotic and stress response regulator). The AsrR regulator used cysteine oxidation to sense the hydrogen peroxide which results in its dissociation to promoter DNA. Transcriptome analysis showed that the AsrR regulon was composed of 181 genes, including representing functionally diverse groups involved in pathogenesis, antibiotic and antimicrobial peptide resistance, oxidative stress, and adaptive responses. Consistent with the upregulated expression of the pbp5 gene, encoding a low-affinity penicillin-binding protein, the asrR null mutant was found to be more resistant to \u3b2-lactam antibiotics. Deletion of asrR markedly decreased the bactericidal activity of ampicillin and vancomycin, which are both commonly used to treat infections due to enterococci, and also led to over-expression of two major adhesins, acm and ecbA, which resulted in enhanced in vitro adhesion to human intestinal cells. Additional pathogenic traits were also reinforced in the asrR null mutant including greater capacity than the parental strain to form biofilm in vitro and greater persistance in Galleria mellonella colonization and mouse systemic infection models. Despite overexpression of oxidative stress-response genes, deletion of asrR was associated with a decreased oxidative stress resistance in vitro, which correlated with a reduced resistance to phagocytic killing by murine macrophages. Interestingly, both strains showed similar amounts of intracellular reactive oxygen species. Finally, we observed a mutator phenotype and enhanced DNA transfer frequencies in the asrR deleted strain. These data indicate that AsrR plays a major role in antimicrobial resistance and adaptation for survival within the host, thereby contributes importantly to the opportunistic traits of E. faecium

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Narrowing Uncertainties of Climate Projections using Data-driven Methods: the AMOC Case Study

International audienc

Narrowing Uncertainties of Climate Projections using Data-driven Methods: the AMOC Case Study

International audienc

Selecting and weighting dynamical models using data-driven approaches

In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. To obtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted. The simplest method, namely the model democracy, gives equal weights to all models, while more advanced approaches base weights on agreement with available observations. Here, we focus on determining weights for various versions of an idealized model of Atlantic Meridional Overturning Circulation. This is done by assessing their performance against synthetic observations (generated from one of the model versions) within a data assimilation framework using EnKF. In contrast to traditional data assimilation, we implement data-driven forecasts using the analog method based on catalogs of short-term trajectories. This approach allows us to efficiently emulate the model's dynamics while keeping computational costs low. For each model version, we compute a local performance metric, known as the contextual model evidence, to compare observations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences in model dynamics and considers forecast and observation uncertainties. Finally, the weights are calculated using both model performance and model codependency, and then evaluated on climatologies of long-term simulations. Results show good performance in identifying numerical simulations that best replicate observed short-term variations. Additionally, it outperforms benchmark approaches such as model democracy or climatologies-based strategies when reconstructing missing distributions. These findings encourage the application of the proposed methodology to more complex datasets in the future, like climate simulations

Narrowing Uncertainties of Climate Projections using Data-driven Methods: the AMOC Case Study

International audienc

Narrowing Uncertainties of Climate Projections using Data-driven Methods: the AMOC Case Study

International audienc

Inactivation of Enveloped Bovine Viral Diarrhea Virus and Non-Enveloped Porcine Parvovirus Using Low-Pressure Non-Thermal Plasma

International audienceAs the worldwide population has been experiencing since 2020, viruses represent a serious threat to global well-being. To avoid viral transmission through surgery or medical examination, sterilization of medical material is needed. From emerging sterilization processes, the use of nonthermal plasma (NTP) arises as a promising technique to efficiently reduce microbial burden on medical devices, including new complex polymers as thermosensitive ones. Thus, we evaluated the antiviral efficacy of a low-pressure NTP process taking place in a sealed bag. For this purpose, two different plasmas, O2 100% plasma and Ar 80%–O2 20% plasma, were tested against two viruses: the bovine viral diarrhea virus and the porcine parvovirus, surrogates of human hepatitis C virus and human parvovirus B19, respectively. The efficacy of both NTP treatments on viral load can be detected after only five minutes. Moreover, the longer the NTP treatments last, the more the load decreases. The most effective load reduction was obtained with a 120-min O2 plasma treatment inducing a minimum of four-log viral load reduction. So, this process demonstrated strong virucidal capacity inside a sealed bag and represents a very interesting opportunity in the field of fragile medical devices sterilization or disinfection

Non-Thermal O2 Plasma Efficacy on C. albicans and Its Effect on Denture Base Resin Color

International audienceDenture stomatitis is a disease involving C. albicans, which can affect elderly and immunocompromised people. To avoid any recurrence of this pathology, it is necessary to treat patients regularly and disinfect dentures. However, the denture cleansers’ efficacy is not optimal and often leads to adverse color effects on the denture base resins. The aim of this study was to investigate the efficacy of a low-pressure non-thermal O2 plasma (NTP) treatment on C. albicans seeded on ProBase®Hot resin (Ivoclar Vivadent). The viability reduction of C. albicans was assessed by colony forming units (CFU) analysis and by scanning electron microscopy (SEM). The effect of repeated treatments on the resin color was evaluated by spectrophotometry. The resin samples were placed in a sealed bag in which O2 plasma was generated in low-pressure conditions. The results showed that a 120-min O2 NTP treatment led to a 6-log reduction of C. albicans viability (p < 0.05) and to yeasts’ major alterations observed by SEM. Furthermore, significant slight color changes of the resin (DE00 = 1.33) were noted only after six plasma treatments (p < 0.05). However, the denture aesthetic was preserved, as the color changes were not perceptible and remained below the acceptability threshold (DE00 < 4)