Transgenic mice carrying site-specific genome modifications (knockout, knock-in) are of vital importance for dissecting complex biological systems as well as for modeling human diseases and testing therapeutic strategies. Recent advances in the use of designer nucleases such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) 9 system for site-specific genome engineering open the possibility to perform rapid targeted genome modification in virtually any laboratory species without the need to rely on embryonic stem (ES) cell technology. A genome editing experiment typically starts with identification of designer nuclease target sites within a gene of interest followed by construction of custom DNA-binding domains to direct nuclease activity to the investigator-defined genomic locus. Designer nuclease plasmids are in vitro transcribed to generate mRNA for microinjection of fertilized mouse oocytes. Here, we provide a protocol for achieving targeted genome modification by direct injection of TALEN mRNA into fertilized mouse oocytes.
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http://dx.doi.org/10.3791/50930 | DOI Listing |
Appl Biochem Biotechnol
January 2025
CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, 440020, Maharashtra, India.
The present study investigated the genomic and functional potential of Burkholderia contaminans PB_AQ24, a bacterial strain isolated from the municipal solid waste dumpsite, for boosting the growth of Dendrocalamus strictus (Male bamboo) seedlings. The isolated strain exhibited high potency for metal solubilization and ACC (1-Aminocyclopropane-1-carboxylate) deaminase activity. Its genome harbored diverse genes responsible for nitrogen and phosphorus utilization (trpABCDES, iaaH, acdS, pstABCS, phoAUD, pqqABCDE, kdpABC, gln, and nirBD) and also an abundance of heavy metal tolerant genes (ftsH, hptX, iscX-fdx-hscAB-iscAUR, mgtA, corA, and copC).
View Article and Find Full Text PDFMol Plant
January 2025
National Engineering Research Center for Sugarcane, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Breeding and Multiple Utilization of Crops, Ministry of Education, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China. Electronic address:
Adv Sci (Weinh)
January 2025
Institute of Cardiovascular Sciences, St. Boniface Hospital Albrechtsen Research Centre, Biomedical Engineering Program, Department of Physiology and Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, R2H2A6, Canada.
Periodic table of chemical elements serves as the foundation of material chemistry, impacting human health in many different ways. It contributes to the creation, growth, and manipulation of functional metallic, ceramic, metalloid, polymeric, and carbon-based materials on and near an atomic scale. Recent nanotechnology advancements have revolutionized the field of biomedical engineering to tackle longstanding clinical challenges.
View Article and Find Full Text PDFPlant Methods
January 2025
College of Horticulture and Landscape Architecture, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Background: Virus-induced gene silencing (VIGS) is a rapid and powerful method for gene functional analysis in plants that pose challenges in stable transformation. Numerous VIGS systems based on Agrobacterium infiltration has been widely developed for tender tissues of various plant species, yet none is available for recalcitrant perennial woody plants with firmly lignified capsules, such as tea oil camellia. Therefore, there is an urgent need for an efficient, robust, and cost-effective VIGS system for recalcitrant tissues.
View Article and Find Full Text PDFMicrobiome
January 2025
Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
Background: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.
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