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Genome shuffling leads to rapid phenotypic improvement in bacteria

Author

Listed:
  • Ying-Xin Zhang

    (Maxygen)

  • Kim Perry

    (Maxygen)

  • Victor A. Vinci

    (Eli Lilly and Company, Lilly Corporate Center)

  • Keith Powell

    (Maxygen)

  • Willem P. C. Stemmer

    (Maxygen)

  • Stephen B. del Cardayré

    (Maxygen)

Abstract

For millennia, selective breeding, on the basis of biparental mating, has led to the successful improvement of plants and animals to meet societal needs1. At a molecular level, DNA shuffling mimics, yet accelerates, evolutionary processes, and allows the breeding and improvement of individual genes and subgenomic DNA fragments. We describe here whole-genome shuffling; a process that combines the advantage of multi-parental crossing allowed by DNA shuffling with the recombination of entire genomes normally associated with conventional breeding. We show that recursive genomic recombination within a population of bacteria can efficiently generate combinatorial libraries of new strains. When applied to a population of phenotypically selected bacteria, many of these new strains show marked improvements in the selected phenotype. We demonstrate the use of this approach through the rapid improvement of tylosin production from Streptomyces fradiae. This approach has the potential to facilitate cell and metabolic engineering and provide a non-recombinant alternative to the rapid production of improved organisms.

Suggested Citation

  • Ying-Xin Zhang & Kim Perry & Victor A. Vinci & Keith Powell & Willem P. C. Stemmer & Stephen B. del Cardayré, 2002. "Genome shuffling leads to rapid phenotypic improvement in bacteria," Nature, Nature, vol. 415(6872), pages 644-646, February.
  • Handle: RePEc:nat:nature:v:415:y:2002:i:6872:d:10.1038_415644a
    DOI: 10.1038/415644a
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    Cited by:

    1. Ali Mahmoudi & Jere Koskela & Jerome Kelleher & Yao-ban Chan & David Balding, 2022. "Bayesian inference of ancestral recombination graphs," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-15, March.
    2. Tan, Taison & Frenkel, Daan & Gupta, Vishal & Deem, Michael W., 2005. "Length, protein–protein interactions, and complexity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 350(1), pages 52-62.
    3. Wu, Bo & Wang, Yan-Wei & Dai, Yong-Hua & Song, Chao & Zhu, Qi-Li & Qin, Han & Tan, Fu-Rong & Chen, Han-Cheng & Dai, Li-Chun & Hu, Guo-Quan & He, Ming-Xiong, 2021. "Current status and future prospective of bio-ethanol industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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