Genome-wide screening is powerful method used to identify genes and pathways associated with a phenotype of interest. The simple eukaryote Dictyostelium discoideum has a unique life cycle and is often used as a crucial research model for a wide range of biological processes and rare metabolites. To address the inadequacies of conventional genetic screening approaches, we developed a highly efficient CRISPR/Cas9-based genome-wide screening system for Dictyostelium. A genome-wide library of 27,405 gRNAs and a kinase library of 4,582 gRNAs were compiled and mutant pools were generated. The resulting mutants were screened for defects in cell growth and more than 10 candidate genes were identified. Six of these were validated and five recreated mutants presented with growth abnormalities. Finally, the genes implicated in developmental defects were screened to identify the unknown genes associated with a phenotype of interest. These findings demonstrate the potential of the CRISPR/Cas9 system as an efficient genome-wide screening method.
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http://dx.doi.org/10.1038/s41598-022-15500-3 | DOI Listing |
Fish Shellfish Immunol
January 2025
Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao 266071, China. Electronic address:
Fibrinogen-related domain (FReD) containing proteins are an evolutionarily conserved immune gene family characterized by the C-terminal fibrinogen (FBG) and diverse N-terminal domains. To understand the complexity of this family in crustaceans, we performed genome screening and identified 43 full-length FReDs encoding genes in Litopenaeus vannamei. Structural classification analysis revealed these putative FReDs could be divided into six types, including two reported types (LvFReDI and II) and four new types (LvFReDIII-VI).
View Article and Find Full Text PDFJAMA Netw Open
December 2024
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis.
Importance: Identification of individuals at high risk of alcohol use disorder (AUD) and subsequent application of prevention and intervention programs has been reported to decrease the incidence of AUD. The polygenic score (PGS), which measures an individual's genetic liability to a disease, can potentially be used to evaluate AUD risk.
Objective: To assess the estimability and generalizability of the PGS, compared with family history and ADH1B, in evaluating the risk of AUD among populations of European ancestry.
Background: Alzheimer's disease (AD), a complex and polygenic disease with a considerable hereditary component (60-80%), is a progressive neurodegenerative disorder characterized by concealed onset, and individuals often have significant cognitive impairment and histopathological changes in the brain before overt clinical diagnosis. However, the correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages.
View Article and Find Full Text PDFBackground: Studies have shown physical activity (PA) patterns are heritable traits and are correlated with several known genetic risk factors including APOE, the best-known gene associated with Alzheimer's Disease (AD). SPARE-AD was a previously developed machine learning index known to be sensitive to AD-like brain atrophy. However, the relationship between genetic variants, physical activity patterns and AD-related neuroimaging features have yet been extensively studied due to the lack of appropriate data and statistical methods for handling complex multimodal data.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Background: Brain morphology changes due to both natural aging and various pathological conditions. We used magnetic resonance imaging (MRI) and artificial intelligence (AI) to derive three brain age gaps (Wen et al., 2023b) [gray matter (GM), white matter (WM), and functional connectivity (FC)-BAG] for brain aging and 9 dimensional neuroimaging endophenotypes (Wen et al.
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