108 research outputs found

    FORGETTER2 protein phosphatase and phospholipase D modulate heat stress memory in Arabidopsis

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    Summary Plants can mitigate environmental stress conditions through acclimation. In the case of fluctuating stress conditions such as high temperatures, maintaining a stress memory enables a more efficient response upon recurring stress. In a genetic screen for Arabidopsis thaliana mutants impaired in the memory of heat stress (HS) we have isolated the FORGETTER2 (FGT2) gene which encodes a type 2C protein phosphatase (PP2C) of the D-clade. Mutants in fgt2 acquire thermotolerance normally; however, they are defective in the memory of HS. FGT2 interacts with phospholipase Dα2 (PLDα2), which is involved in metabolizing membrane phospholipids, and PLDα2 is also required for HS memory. In summary, we uncover a previously unknown component of HS memory and identify the FGT2 protein phosphatase and PLDα2 as crucial players, suggesting that phosphatidic acid-dependent signaling or membrane composition dynamics underlie HS memory

    Heteromeric HSFA2/HSFA3 complexes drive transcriptional memory after heat stress in <i>Arabidopsis</i>

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    Moderate heat stress primes plants to acquire tolerance to subsequent, more severe heat stress. Here the authors show that the HSFA3 transcription factor forms a heteromeric complex with HSFA2 to sustain activated transcription of genes required for acquired thermotolerance by promoting H3K4 hyper-methylation

    The hydroxyl functionality and a rigid proximal N are required for forming a novel non-covalent quinine-heme complex

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    Quinoline antimalarial drugs bind both monomeric and dimeric forms of free heme, with distinct preferences depending on the chemical environment. Under biological conditions, chloroquine (CQ) appears to prefer to bind to μ-oxo dimeric heme, while quinine (QN) preferentially binds monomer. To further explore this important distinction, we study three newly synthesized and several commercially available QN analogues lacking various functional groups. We find that removal of the QN hydroxyl lowers heme affinity, hemozoin (Hz) inhibition efficiency, and antiplasmodial activity. Elimination of the rigid quinuclidyl ring has similar effects, but elimination of either the vinyl or methoxy group does not. Replacing the quinuclidyl N with a less rigid tertiary aliphatic N only partially restores activity. To further study these trends, we probe drug-heme interactions via NMR studies with both Fe and Zn protoporphyrin IX (FPIX, ZnPIX) for QN, dehydroxyQN (DHQN), dequinuclidylQN (DQQN), and deamino-dequinuclidylQN (DADQQN). Magnetic susceptibility measurements in the presence of FPIX demonstrate that these compounds differentially perturb FPIX monomer-dimer equilibrium. We also isolate the QN-FPIX complex formed under mild aqueous conditions and analyze it by mass spectrometry, as well as fluorescence, vibrational, and solid state NMR spectroscopies. The data elucidate key features of QN pharmacology and allow us to propose a refined model for the preferred binding of QN to monomeric FPIX under biologically relevant conditions. With this model in hand, we also propose how QN, CQ, and amodiaquine (AQ) differ in their ability to inhibit Hz formation

    Evaluation of miCRovascular rarefaction in vascUlar Cognitive Impairment and heArt faiLure (CRUCIAL): Study protocol for an observational study

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    INTRODUCTION: Microvascular rarefaction, the functional reduction in perfused microvessels and structural reduction of microvascular density, seems to be an important mechanism in the pathophysiology of small blood vessel related disorders including vascular cognitive impairment (VCI) due to cerebral small vessel disease and heart failure with preserved ejection fraction (HFpEF). Both diseases share common risk factors including hypertension, diabetes mellitus, obesity, and ageing; in turn, these co-morbidities are associated with microvascular rarefaction. Our consortium aims to investigate novel non-invasive tools to quantify microvascular health and rarefaction in both organs, as well as surrogate biomarkers for cerebral and/or cardiac rarefaction (via sublingual capillary health, vascular density of the retina, and RNA content of circulating extracellular vesicles), and to determine whether microvascular density relates to disease severity. METHODS/DESIGN: The clinical research program of CRUCIAL consists of four observational cohort studies. We aim to recruit 75 VCI patients, 60 HFpEF patients, 60 patients with severe aortic stenosis (AS) undergoing surgical aortic valve replacement as a pressure overload HFpEF model, and 200 elderly participants with mixed comorbidities to serve as controls. Data collected will include medical history, physical examination, cognitive testing, advanced brain and cardiac MRI, ECG, echocardiography, sublingual capillary health, optical coherence tomography angiography (OCTa), extracellular vesicles RNA analysis and myocardial remodelling-related serum biomarkers. The AS cohort undergoing surgery will also have myocardial biopsy for histological microvascular assessment. DISCUSSION: CRUCIAL will examine the pathophysiological role of microvascular rarefaction in VCI and HFpEF using advanced brain and cardiac MRI techniques. Furthermore, we will investigate surrogate biomarkers for non-invasive, faster, easier, and cheaper assessment of microvascular density since these are more likely to be disseminated into widespread clinical practice. If microvascular rarefaction is an early marker of developing small vessel diseases, then measuring rarefaction may allow pre-clinical diagnosis, with implications for screening, risk stratification, and prevention. Further knowledge of the relevance of microvascular rarefaction and its underlying mechanisms may provide new avenues for research and therapeutic targets

    Anti-plasmodial polyvalent interactions in Artemisia annua L. aqueous extract – possible synergistic and resistance mechanisms

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    Artemisia annua hot water infusion (tea) has been used in in vitro experiments against P. falciparum malaria parasites to test potency relative to equivalent pure artemisinin. High performance liquid chromatography (HPLC) and mass spectrometric analyses were employed to determine the metabolite profile of tea including the concentrations of artemisinin (47.5±0.8 mg L-1), dihydroartemisinic acid (70.0±0.3 mg L-1), arteannuin B (1.3±0.0 mg L-1), isovitexin (105.0±7.2 mg L-1) and a range of polyphenolic acids. The tea extract, purified compounds from the extract, and the combination of artemisinin with the purified compounds were tested against chloroquine sensitive and chloroquine resistant strains of P. falciparum using the DNA-intercalative SYBR Green I assay. The results of these in vitro tests and of isobologram analyses of combination effects showed mild to strong antagonistic interactions between artemisinin and the compounds (9-epi-artemisinin and artemisitene) extracted from A. annua with significant (IC50 <1 μM) anti-plasmodial activities for the combination range evaluated. Mono-caffeoylquinic acids, tri-caffeoylquinic acid, artemisinic acid and arteannuin B showed additive interaction while rosmarinic acid showed synergistic interaction with artemisinin in the chloroquine sensitive strain at a combination ratio of 1:3 (artemisinin to purified compound). In the chloroquine resistant parasite, using the same ratio, these compounds strongly antagonised artemisinin anti-plasmodial activity with the exception of arteannuin B, which was synergistic. This result would suggest a mechanism targeting parasite resistance defenses for arteannuin B’s potentiation of artemisinin

    Modern applications of machine learning in quantum sciences

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    In these Lecture Notes, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.Comment: 268 pages, 87 figures. Comments and feedback are very welcome. Figures and tex files are available at https://github.com/Shmoo137/Lecture-Note
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