Evidence map›Paper›PMID 23656325›Full record

ArticleACS synthetic biology2013

Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis.

Jeffrey A Dietrich, David L Shis, Azadeh Alikhani, Jay D Keasling

Open access · greenAbstract read
In one paragraph

Article in ACS synthetic biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed
4.8field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 209 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Balancing Cell Growth and Product Synthesis for Efficient Microbial Cell Factories.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  6. Article
  7. A new paradigm for the regulation of A40926B0 biosynthesis.Synthetic and systems biotechnology · 2025
    Article
  8. Directed evolution of hydrocarbon-producing enzymes.Biotechnology for biofuels and bioproducts · 2025
    Review
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review

25 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Jeffrey A DietrichUCSF-UCB Joint Graduate Group in Bioengineering, Berkeley, CA 94720, USA.
David L Shis
Azadeh Alikhani
Jay D Keasling
Joint BioEnergy Institute · USGraduate Theological Union · USLygos (United States) · US

Funding

APPLIED BIOLOGY AND BIOPROCESS ENGINEERING TRAININGT32GM008352 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI SCHAFFER, DAVID V · 1989 to 2008
$2.0M
NIGMS NIH HHS T32 GM008352
6 · The paper itself

Abstract

Continued advances in metabolic engineering are increasing the number of small molecules being targeted for microbial production. Pathway yields and productivities, however, are often suboptimal, and strain improvement remains a persistent challenge given that the majority of small molecules are difficult to screen for and their biosynthesis does not improve host fitness. In this work, we have developed a generalized approach to screen or select for improved small-molecule biosynthesis using transcription factor-based biosensors. Using a tetracycline resistance gene 3' of a small-molecule inducible promoter, host antibiotic resistance, and hence growth rate, was coupled to either small-molecule concentration in the growth medium or a small-molecule production phenotype. Biosensors were constructed for two important chemical classes, dicarboxylic acids and alcohols, using transcription factor-promoter pairs derived from Pseudomonas putida, Thauera butanivorans, or E. coli. Transcription factors were selected for specific activation by either succinate, adipate, or 1-butanol, and we demonstrate product-dependent growth in E. coli using all three compounds. The 1-butanol biosensor was applied in a proof-of-principle liquid culture screen to optimize 1-butanol biosynthesis in engineered E. coli, identifying a pathway variant yielding a 35% increase in 1-butanol specific productivity through optimization of enzyme expression levels. Lastly, to demonstrate the capacity to select for enzymatic activity, the 1-butanol biosensor was applied as synthetic selection, coupling in vivo 1-butanol biosynthesis to E. coli fitness, and an 120-fold enrichment for a 1-butanol production phenotype was observed following a single round of positive selection.

Indexed as

1-ButanolBiosensing TechniquesDicarboxylic AcidsEscherichia coliEscherichia coli ProteinsGenetic EngineeringMetabolic EngineeringPromoter Regions, GeneticSmall Molecule LibrariesTranscription Factors1-ButanolDicarboxylic AcidsEscherichia coli ProteinsSmall Molecule LibrariesTranscription Factors

Identifiers

PMID23656325
PMCPMC11245165
OpenAlexW2081799587

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.