248 research outputs found

    Substrate Profiling of Tobacco Etch Virus Protease Using a Novel Fluorescence-Assisted Whole-Cell Assay

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    Site-specific proteolysis of proteins plays an important role in many cellular functions and is often key to the virulence of infectious organisms. Efficient methods for characterization of proteases and their substrates will therefore help us understand these fundamental processes and thereby hopefully point towards new therapeutic strategies. Here, a novel whole-cell in vivo method was used to investigate the substrate preference of the sequence specific tobacco etch virus protease (TEVp). The assay, which utilizes protease-mediated intracellular rescue of genetically encoded short-lived fluorescent substrate reporters to enhance the fluorescence of the entire cell, allowed subtle differences in the processing efficiency of closely related substrate peptides to be detected. Quantitative screening of large combinatorial substrate libraries, through flow cytometry analysis and cell sorting, enabled identification of optimal substrates for TEVp. The peptide, ENLYFQG, identical to the protease's natural substrate peptide, emerged as a strong consensus cleavage sequence, and position P3 (tyrosine, Y) and P1 (glutamine, Q) within the substrate peptide were confirmed as being the most important specificity determinants. In position P1′, glycine (G), serine (S), cysteine (C), alanine (A) and arginine (R) were among the most prevalent residues observed, all known to generate functional TEVp substrates and largely in line with other published studies stating that there is a strong preference for short aliphatic residues in this position. Interestingly, given the complex hydrogen-bonding network that the P6 glutamate (E) is engaged in within the substrate-enzyme complex, an unexpectedly relaxed residue preference was revealed for this position, which has not been reported earlier. Thus, in the light of our results, we believe that our assay, besides enabling protease substrate profiling, also may serve as a highly competitive platform for directed evolution of proteases and their substrates

    Food-induced Emotional Resonance Improves Emotion Recognition

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    The effect of food substances on emotional states has been widely investigated, showing, for example, that eating chocolate is able to reduce negative mood. Here, for the first time, we have shown that the consumption of specific food substances is not only able to induce particular emotional states, but more importantly, to facilitate recognition of corresponding emotional facial expressions in others. Participants were asked to perform an emotion recognition task before and after eating either a piece of chocolate or a small amount of fish sauce – which we expected to induce happiness or disgust, respectively. Our results showed that being in a specific emotional state improves recognition of the corresponding emotional facial expression. Indeed, eating chocolate improved recognition of happy faces, while disgusted expressions were more readily recognized after eating fish sauce. In line with the embodied account of emotion understanding, we suggest that people are better at inferring the emotional state of others when their own emotional state resonates with the observed one

    Harmonization of human biomonitoring studies in Europe: characteristics of the HBM4EU-aligned studies participants

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    Human biomonitoring has become a pivotal tool for supporting chemicals' policies. It provides information on real-life human exposures and is increasingly used to prioritize chemicals of health concern and to evaluate the success of chemical policies. Europe has launched the ambitious REACH program in 2007 to improve the protection of human health and the environment. In October 2020 the EU commission published its new chemicals strategy for sustainability towards a toxic-free environment. The European Parliament called upon the commission to collect human biomonitoring data to support chemical's risk assessment and risk management. This manuscript describes the organization of the first HBM4EU-aligned studies that obtain comparable human biomonitoring (HBM) data of European citizens to monitor their internal exposure to environmental chemicals. The HBM4EU-aligned studies build on existing HBM capacity in Europe by aligning national or regional HBM studies. The HBM4EU-aligned studies focus on three age groups: children, teenagers, and adults. The participants are recruited between 2014 and 2021 in 11 to 12 primary sampling units that are geographically distributed across Europe. Urine samples are collected in all age groups, and blood samples are collected in children and teenagers. Auxiliary information on socio-demographics, lifestyle, health status, environment, and diet is collected using questionnaires. In total, biological samples from 3137 children aged 6-12 years are collected for the analysis of biomarkers for phthalates, HEXAMOLL((R)) DINCH, and flame retardants. Samples from 2950 teenagers aged 12-18 years are collected for the analysis of biomarkers for phthalates, Hexamoll((R)) DINCH, and per- and polyfluoroalkyl substances (PFASs), and samples from 3522 adults aged 20-39 years are collected for the analysis of cadmium, bisphenols, and metabolites of polyaromatic hydrocarbons (PAHs). The children's group consists of 50.4% boys and 49.5% girls, of which 44.1% live in cities, 29.0% live in towns/suburbs, and 26.8% live in rural areas. The teenagers' group includes 50.6% girls and 49.4% boys, with 37.7% of residents in cities, 31.2% in towns/suburbs, and 30.2% in rural areas. The adult group consists of 52.6% women and 47.4% men, 71.9% live in cities, 14.2% in towns/suburbs, and only 13.4% live in rural areas. The study population approaches the characteristics of the general European population based on age-matched EUROSTAT EU-28, 2017 data; however, individuals who obtained no to lower educational level (ISCED 0-2) are underrepresented. The data on internal human exposure to priority chemicals from this unique cohort will provide a baseline for Europe's strategy towards a non-toxic environment and challenges and recommendations to improve the sampling frame for future EU-wide HBM surveys are discussed

    Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.

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    Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA
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