Co-founding ant queens prevent disease by performing prophylactic undertaking behaviour

Abstract Background Social insects form densely crowded societies in environments with high pathogen loads, but have evolved collective defences that mitigate the impact of disease. However, colony-founding queens lack this protection and suffer high rates of mortality. The impact of pathogens may be exacerbated in species where queens found colonies together, as healthy individuals may contract pathogens from infectious co-founders. Therefore, we tested whether ant queens avoid founding colonies with pathogen-exposed conspecifics and how they might limit disease transmission from infectious individuals. Results Using Lasius niger queens and a naturally infecting fungal pathogen Metarhizium brunneum, we observed that queens were equally likely to found colonies with another pathogen-exposed or sham-treated queen. However, when one queen died, the surviving individual performed biting, burial and removal of the corpse. These undertaking behaviours were performed prophylactically, i.e. targeted equally towards non-infected and infected corpses, as well as carried out before infected corpses became infectious. Biting and burial reduced the risk of the queens contracting and dying from disease from an infectious corpse of a dead co-foundress. Conclusions We show that co-founding ant queens express undertaking behaviours that, in mature colonies, are performed exclusively by workers. Such infection avoidance behaviours act before the queens can contract the disease and will therefore improve the overall chance of colony founding success in ant queens

Association between age and the host response in critically ill patients with sepsis

Background: The association of ageing with increased sepsis mortality is well established. Nonetheless, current investigations on the influence of age on host response aberrations are largely limited to plasma cytokine levels while neglecting other pathophysiological sepsis domains like endothelial cell activation and function, and coagulation activation. The primary objective of this study was to gain insight into the association of ageing with aberrations in key host response pathways and blood transcriptomes in sepsis.Methods: We analysed the clinical outcome (n=1952), 16 plasma biomarkers providing insight in deregulation of specific pathophysiological domains (n=899), and blood leukocyte transcriptomes (n=488) of sepsis patients stratified according to age decades. Blood transcriptome results were validated in an independent sepsis cohort and compared with healthy individuals. se.Results: Older age was associated with increased mortality independent of comorbidities and disease severity. Ageing was associated with lower endothelial cell activation and dysfunction, and similar inflammation and coagulation activation, despite higher disease severity scores. Blood leukocytes of patients≥70 years, compared to patients<50 years, showed decreased expression of genes involved in cytokine signaling, and innate and adaptive immunity, and increased expression of genes involved in hemostasis and endothelial cell activation. The diminished expression of gene pathways related to innate immunity and cytokine signaling in subjects≥70 years was sepsis-induced, as healthy subjects≥70 years showed enhanced expression of these pathways compared to healthy individuals<50 years.Conclusions: This study provides novel evidence that older age is associated with relatively mitigated sepsis-induced endothelial cell activation and dysfunction, and a blood leukocyte transcriptome signature indicating impaired innate immune and cytokine signaling. These data suggest that age should be considered in patient selection in future sepsis trials targeting the immune system and/or the endothelial cell response.peer-reviewe

SAMHD1 is a biomarker for cytarabine response and a therapeutic target in acute myeloid leukemia.

The nucleoside analog cytarabine (Ara-C) is an essential component of primary and salvage chemotherapy regimens for acute myeloid leukemia (AML). After cellular uptake, Ara-C is converted into its therapeutically active triphosphate metabolite, Ara-CTP, which exerts antileukemic effects, primarily by inhibiting DNA synthesis in proliferating cells. Currently, a substantial fraction of patients with AML fail to respond effectively to Ara-C therapy, and reliable biomarkers for predicting the therapeutic response to Ara-C are lacking. SAMHD1 is a deoxynucleoside triphosphate (dNTP) triphosphohydrolase that cleaves physiological dNTPs into deoxyribonucleosides and inorganic triphosphate. Although it has been postulated that SAMHD1 sensitizes cancer cells to nucleoside-analog derivatives through the depletion of competing dNTPs, we show here that SAMHD1 reduces Ara-C cytotoxicity in AML cells. Mechanistically, dGTP-activated SAMHD1 hydrolyzes Ara-CTP, which results in a drastic reduction of Ara-CTP in leukemic cells. Loss of SAMHD1 activity-through genetic depletion, mutational inactivation of its triphosphohydrolase activity or proteasomal degradation using specialized, virus-like particles-potentiates the cytotoxicity of Ara-C in AML cells. In mouse models of retroviral AML transplantation, as well as in retrospective analyses of adult patients with AML, the response to Ara-C-containing therapy was inversely correlated with SAMHD1 expression. These results identify SAMHD1 as a potential biomarker for the stratification of patients with AML who might best respond to Ara-C-based therapy and as a target for treating Ara-C-refractory AML

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Dipole-coupled Nano-Ring(s) of Quantum Emitters

Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüftInnsbruck, Univ., Masterarb., 2019(VLID)448038

Thermoresponsive Microgel-Based Free-Standing Membranes: Influence of Different Microgel Cross-Linkers on Membrane Function

Dirksen M, Fandrich P, Gött-Zink L, Cremer J, Anselmetti D, Hellweg T. Thermoresponsive Microgel-Based Free-Standing Membranes: Influence of Different Microgel Cross-Linkers on Membrane Function. Langmuir. 2022: acs.langmuir.1c02195

Equivariant Graph Neural Networks for Toxicity Prediction

Predictive modeling for toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum mechanical properties of molecules. Inspired by this, we investigate the performance of EGNNs to construct reliable ML models for toxicity prediction. We use the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity datasets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most datasets comparable to state-of-the-art models. We also test a physicochemical property, namely the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and, thus, increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse datasets, EGNNs will be an essential tool in this domain

Equivariant graph neural networks for toxicity prediction

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.This research used computational resources provided by the High-Performance Center (HPC) at the University of Luxembourg. J.C. received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions grant agreement “Advanced machine learning for Innovative Drug Discovery (AIDD)” No. 956832. L.M.S. thanks S. Goger for fruitful discussions about the influence of functional groups in toxicity prediction. G.D.F. acknowledges funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 823712; and the project PID2020-116564GB-I00 has been funded by MCIN/AEI/10.13039/501100011033