Overcoming genetic heterogeneity in industrial fermentations

Peter Rugbjerg, Morten Otto Alexander Sommer*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Engineering the synthesis of massive amounts of therapeutics, enzymes or commodity chemicals can select for subpopulations of nonproducer cells, owing to metabolic burden and product toxicity. Deep DNA sequencing can be used to detect undesirable genetic heterogeneity in producer populations and diagnose associated genetic error modes. Hotspots of genetic heterogeneity can pinpoint mechanisms that underlie load problems and product toxicity. Understanding genetic heterogeneity will inform metabolic engineering and synthetic biology strategies to minimize the emergence of nonproducer mutants in scaled-up fermentations and maximize product quality and yield.
Original languageEnglish
JournalNature Biotechnology
Volume37
Pages (from-to)869-876
Number of pages8
ISSN1087-0156
DOIs
Publication statusPublished - 2019

Cite this

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title = "Overcoming genetic heterogeneity in industrial fermentations",
abstract = "Engineering the synthesis of massive amounts of therapeutics, enzymes or commodity chemicals can select for subpopulations of nonproducer cells, owing to metabolic burden and product toxicity. Deep DNA sequencing can be used to detect undesirable genetic heterogeneity in producer populations and diagnose associated genetic error modes. Hotspots of genetic heterogeneity can pinpoint mechanisms that underlie load problems and product toxicity. Understanding genetic heterogeneity will inform metabolic engineering and synthetic biology strategies to minimize the emergence of nonproducer mutants in scaled-up fermentations and maximize product quality and yield.",
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Overcoming genetic heterogeneity in industrial fermentations. / Rugbjerg, Peter; Sommer, Morten Otto Alexander.

In: Nature Biotechnology, Vol. 37, 2019, p. 869-876.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Sommer, Morten Otto Alexander

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AB - Engineering the synthesis of massive amounts of therapeutics, enzymes or commodity chemicals can select for subpopulations of nonproducer cells, owing to metabolic burden and product toxicity. Deep DNA sequencing can be used to detect undesirable genetic heterogeneity in producer populations and diagnose associated genetic error modes. Hotspots of genetic heterogeneity can pinpoint mechanisms that underlie load problems and product toxicity. Understanding genetic heterogeneity will inform metabolic engineering and synthetic biology strategies to minimize the emergence of nonproducer mutants in scaled-up fermentations and maximize product quality and yield.

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