ArticleJournal of industrial microbiology & biotechnology2012
Genome shuffling of Bacillus amyloliquefaciens for improving antimicrobial lipopeptide production and an analysis of relative gene expression using FQ RT-PCR.
Article in Journal of industrial microbiology & biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Enhanced Lichenysin Production in Bacillus licheniformis by Physicochemical Mutagenesis Combined with High-Throughput Screening.Current microbiology · 2026Article
- The Characterization, Biological Activities, and Potential Applications of the Antimicrobial Peptides Derived from Bacillus spp.: A Comprehensive Review.Probiotics and antimicrobial proteins · 2025Review
- Advance of tolerance engineering on microbes for industrial production.Synthetic and systems biotechnology · 2023Review
- Antimicrobial peptides from Bacillus spp. and strategies to enhance their yield.Applied microbiology and biotechnology · 2023Review
- Lesson from Ecotoxicity: Revisiting the Microbial Lipopeptides for the Management of Emerging Diseases for Crop Protection.International journal of environmental research and public health · 2020Review
- Differential proteomics research of Bacillus amyloliquefaciens and its genome-shuffled saltant for improving fengycin production.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2018Article
- Genomics-guided discovery and structure identification of cyclic lipopeptides from the Bacillus siamensis JFL15.PloS one · 2018Article
- Improving Iturin A Production ofFrontiers in microbiology · 2018Article
- Improvement of Bacillus subtilis for poly-γ-glutamic acid production by genome shuffling.Microbial biotechnology · 2016Article
- Differential proteomics analysis of Bacillus amyloliquefaciens and its genome-shuffled mutant for improving surfactin production.International journal of molecular sciences · 2014Article
- Improved FK506 production by the precursors and product-tolerant mutant of Streptomyces tsukubaensis based on genome shuffling and dynamic fed-batch strategies.Journal of industrial microbiology & biotechnology · 2014Article
- Enhanced succinic acid production by Actinobacillus succinogenes after genome shuffling.Journal of industrial microbiology & biotechnology · 2013Article
- Streptomycin resistance-aided genome shuffling to improve doramectin productivity of Streptomyces avermitilis NEAU1069.Journal of industrial microbiology & biotechnology · 2013Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Genome shuffling is an efficient approach for the rapid improvement of the yield of secondary metabolites. This study was undertaken to enhance the yield of surfactin produced by Bacillus amyloliquefaciens ES-2-4 using genome shuffling and to examine changes in SrfA expression of the improved phenotype at the transcriptional level. Six strains with subtle improvements in lipopeptide yield were obtained from populations generated by ultraviolet irradiation, nitrosoguanidine, and ion beam mutagenesis. These strains were then subjected to recursive protoplast fusion. A strain library that was likely to yield positive colonies was created by fusing the lethal protoplasts obtained from both ultraviolet irradiation and heat treatments. After two rounds of genome shuffling, a high-yield recombinant F2-38 strain that exhibited 3.5- and 10.3-fold increases in surfactin production in shake flask and fermenter respectively, was obtained. Comparative analysis of synthetase gene expression was conducted between the initial and shuffled strains using FQ (fluorescent quantitation) RT-PCR. Delta CT (threshold cycle) relative quantitation analysis revealed that surfactin synthetase gene (srfA) expression at the transcriptional level in the F2-38 strain was 15.7-fold greater than in the ES-2-4 wild-type. The shuffled strain has a potential application in food and pharmaceutical industries. At the same time, the analysis of improved phenotypes will provide more valuable data for inverse metabolic engineering.
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