Scoring changes in enzyme or pathway performance by their effect on growth behavior is a widely applied strategy for identifying improved biocatalysts. While in directed evolution this strategy is powerful in removing non-functional catalysts in selections, measuring subtle differences in growth behavior remains difficult at high throughput, as it is difficult to focus metabolic control on only one or a few enzymatic steps over the entire process of growth-based discrimination. Here, we demonstrate successful miniaturization of a growth-based directed enzyme evolution process. For cultivation of library clones we employed optically clear gel-like microcarriers of nanoliter volume (NLRs) as reaction vessels and used fluorescence-assisted particle sorting to estimate the growth behavior of each of the gel-embedded clones in a highly parallelized fashion. We demonstrate that the growth behavior correlates with the desired improvements in enzyme performance and that we can fine-tune selection stringency by including an antimetabolite in the assay. As a model enzyme reaction, we improve the racemization of ornithine, a possible starting block for the large-scale synthesis of sulphostin, by a broad-spectrum amino acid racemase and confirm the discriminatory power by showing that even moderately improved enzyme variants can be readily identified.
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http://dx.doi.org/10.1016/j.ymben.2020.01.003 | DOI Listing |
Dev Psychopathol
January 2025
Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA.
Polygenic scores (PGSs) have garnered increasing attention in the clinical sciences due to their robust prediction signals for psychopathology, including externalizing (EXT) behaviors. However, studies leveraging PGSs have rarely accounted for the phenotypic and developmental heterogeneity in EXT outcomes. We used the National Longitudinal Study of Adolescent to Adult Health (analytic = 4,416), spanning ages 13 to 41, to examine associations between EXT PGSs and trajectories of antisocial behaviors (ASB) and substance use behaviors (SUB) identified via growth mixture modeling.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Agronomy, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China.
The HAK/KUP/KT (High-affinity K transporters/K uptake permeases/K transporters) is the largest and most dominant potassium transporter family in plants, playing a crucial role in various biological processes. However, our understanding of HAK/KUP/KT gene family in potato ( L.) remains limited and unclear.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Agriculture and Biology, Liaocheng University, Liaocheng, China.
The wall-associated kinase (WAK) gene family encodes functional cell wall-related proteins. These genes are widely presented in plants and serve as the receptors of plant cell membranes, which perceive the external environment changes and activate signaling pathways to participate in plant growth, development, defense, and stress response. However, the WAK gene family and the encoded proteins in soybean (Glycine max (L.
View Article and Find Full Text PDFJ Mammal
February 2025
Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, United States.
Animals living in seasonal environments have adopted a wide array of tactics used to deal with seasonal resource scarcity. Many species migrate between habitats to reach areas where food resources are more plentiful as an attempt to address energetic demands through foraging. We assessed the winter behavioral adaptations of Caribou (), a large ungulate inhabiting Arctic and sub-Arctic regions known for seasonal resource scarcity.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Independent Researcher, Hamburg, Germany.
Introduction: Artificial Intelligence (AI) is a transformative technology impacting various sectors of society and the economy. Understanding the factors influencing AI adoption is critical for both research and practice. This study focuses on two key objectives: (1) validating an extended version of the Technology Acceptance Model (TAM) in the context of AI by integrating the Big Five personality traits and AI mindset, and (2) conducting an exploratory k-prototype analysis to classify AI adopters based on demographics, AI-related attitudes, and usage patterns.
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