A Comprehensive, CRISPR-based Functional Analysis of Essential Genes in Bacteria
By J. Peters, Alexandre Colavin, Handuo Shi, Tomasz L. Czarny, Matthew H. Larson, Spencer S. Wong, John S. Hawkins, Candy H. S. Lu, B. Koo, Elizabeth Marta and 7 more. Cell, 2016
684 citations recorded by the source corpus.
Summary Essential gene functions underpin the core reactions required for cell viability, but their contributions and relationships are poorly studied in vivo. Using CRISPR interference, we created knockdowns of every essential gene in Bacillus subtilis and probed their phenotypes. Our high-confidence essential gene network, established using chemical genomics, showed extensive interconnections among distantly related processes and identified modes of action for uncharacterized antibiotics. Importantly, mild knockdown of essential gene functions significantly reduced stationary phase survival without affecting maximal growth rate, suggesting that essential protein levels are set to maximize outgrowth from stationary phase. Finally, high-throughput microscopy indicated that cell morphology is relatively insensitive to mild knockdown but profoundly affected by depletion of gene function, revealing intimate connections between cell growth and shape. Our results provide a framework for systematic investigation of essential gene functions in vivo that is broadly applicable to diverse microorganisms and amenable to comparative analysis.
Subfields: Ecology, Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3a19ddb149a319a23c17afc8208ef90e5bcec022
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Citations in the corpus
- Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection (2016, background): “about the conditional distribution of the response, including: When we in fact know exactly the covariate distribution because we control it, such as in gene knockout experiments (Cong et al., 2013; Peters et al., 2016), genetic crossing experiments (Haldane and Waddington, 1931), or sensitivity ana”
- Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection (2016, background): “When are our assumptions useful? We do not claim our assumptions will always be appropriate, but there are important cases when it is reasonable to think we know much more about the covariate distribution than about the conditional distribution of the response, including: • When we in fact know exac”