An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast

Tim Snoek, David Romero-Suarez, Jie Zhang, Francesca Ambri, Mette L. Skjoedt, Suresh Sudarsan, Michael K. Jensen, Jay D. Keasling

Research output: Contribution to journalLetterResearchpeer-review

Abstract

Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.
Original languageEnglish
JournalA C S Synthetic Biology
Volume7
Issue number4
Pages (from-to)995-1003
ISSN2161-5063
DOIs
Publication statusPublished - 2018

Keywords

  • Biosensor
  • Evolution
  • Metabolic engineering
  • Sustainability
  • Transcriptional activator
  • Yeast

Cite this

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title = "An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast",
abstract = "Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.",
keywords = "Biosensor, Evolution, Metabolic engineering, Sustainability, Transcriptional activator, Yeast",
author = "Tim Snoek and David Romero-Suarez and Jie Zhang and Francesca Ambri and Skjoedt, {Mette L.} and Suresh Sudarsan and Jensen, {Michael K.} and Keasling, {Jay D.}",
year = "2018",
doi = "10.1021/acssynbio.7b00439",
language = "English",
volume = "7",
pages = "995--1003",
journal = "A C S Synthetic Biology",
issn = "2161-5063",
publisher = "American Chemical Society",
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An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast. / Snoek, Tim; Romero-Suarez, David; Zhang, Jie; Ambri, Francesca; Skjoedt, Mette L.; Sudarsan, Suresh; Jensen, Michael K.; Keasling, Jay D.

In: A C S Synthetic Biology, Vol. 7, No. 4, 2018, p. 995-1003.

Research output: Contribution to journalLetterResearchpeer-review

TY - JOUR

T1 - An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast

AU - Snoek, Tim

AU - Romero-Suarez, David

AU - Zhang, Jie

AU - Ambri, Francesca

AU - Skjoedt, Mette L.

AU - Sudarsan, Suresh

AU - Jensen, Michael K.

AU - Keasling, Jay D.

PY - 2018

Y1 - 2018

N2 - Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.

AB - Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.

KW - Biosensor

KW - Evolution

KW - Metabolic engineering

KW - Sustainability

KW - Transcriptional activator

KW - Yeast

U2 - 10.1021/acssynbio.7b00439

DO - 10.1021/acssynbio.7b00439

M3 - Letter

VL - 7

SP - 995

EP - 1003

JO - A C S Synthetic Biology

JF - A C S Synthetic Biology

SN - 2161-5063

IS - 4

ER -