(group B [GBS]) is a cause of severe infections, particularly during the newborn period. While methods exist for generating chromosomal mutations in GBS, they are cumbersome and inefficient and present significant challenges if the goal is to study subtle mutations, such as single-base-pair polymorphisms. To address this problem, we have developed an efficient and flexible GBS mutagenesis protocol based on sucrose counterselection against levansucrase (SacB) expressed from a temperature-selective shuttle vector. GBS containing the SacB expression cassette demonstrates lethal sensitivity to supplemental sucrose whether the plasmid DNA is replicating outside of the chromosome or has been integrated during a crossover event. Transmission electron microscopy shows that SacB-mediated lethal sucrose sensitivity results from the accumulation of inclusion bodies that eventually lead to complete degradation of normal cellular architecture and subsequent lysis. We used this new mutagenesis technique to generate an in-frame, allelic exchange knockout of the GBS sortase gene , demonstrating that >99% of colonies that emerge from our protocol had the expected knockout phenotype and that among a subset tested by sequencing, 100% had the correct genotype. We also generated barcoded nonsense mutations in the gene in two GBS strains, showing that the approach can be used to make small, precise chromosomal mutations. The ability to generate chromosomal mutations is fundamental to microbiology. Historically, however, GBS pathogenesis research has been made challenging by the relative genetic intractability of the organism. Generating a single knockout in GBS using traditional techniques can take many months, with highly variable success rates. Furthermore, traditional methods do not offer a straightforward way to generate single-base-pair polymorphisms or other subtle changes, especially to noncoding regions of the chromosome. We have developed a new sucrose counterselection-based method that permits rapid, efficient, and flexible GBS mutagenesis. Our technique requires no additional equipment beyond what is needed for traditional approaches. We believe that it will catalyze rapid advances in GBS genetics research by significantly easing the path to generating mutants.
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http://dx.doi.org/10.1128/AEM.03009-18 | DOI Listing |
PNAS Nexus
January 2025
Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
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Empa Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Fast and accurate representation of heat transfer in laser powder-bed fusion of metals (PBF-LB/M) is essential for thermo-mechanical analyses. As an example, it benefits the detection of thermal hotspots at the design stage. While traditional physics-based numerical approaches such as the finite element (FE) method are applicable to a wide variety of problems, they are computationally too expensive for PBF-LB/M due to the space- and time-discretization requirements.
View Article and Find Full Text PDFData Min Knowl Discov
January 2025
CWI, Amsterdam, The Netherlands.
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View Article and Find Full Text PDFBiophys Rev (Melville)
March 2025
School of Physics, Australian Centre for Microscopy and Microanalysis, Sydney Nano Institute, The University of Sydney, Sydney, NSW 2006, Australia.
The accurate detection of x-rays enables broad applications in various fields, including medical radiography, safety and security screening, and nondestructive inspection. Medical imaging procedures require the x-ray detection devices operating with low doses and high efficiency to reduce radiation health risks, as well as expect the flexible or wearable ones that offer more comfortable and accurate diagnosis experiences. Recently, halide perovskites have shown promising potential in high-performance, cost-effective x-ray detection owing to their attractive features, such as strong x-ray absorption, high-mobility-lifetime product, tunable bandgap, fast response, as well as low-cost raw materials, facile processing, and excellent flexibility.
View Article and Find Full Text PDFEvolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequence alignments.
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