The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Transcription Factor-Based Screens and Synthetic Selections for Microbial Small-Molecule Biosynthesis
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Zhang, J.-H., Chung, T. D. Y., & Oldenburg, K. R. (1999) · SLAS Discovery · doi:10.1177/108705719900400206 · Cited by 6,847
Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels
Atsumi, S., Hanai, T., & Liao, J. C. (2008) · Nature · doi:10.1038/nature06450 · Cited by 1,945
Programming cells by multiplex genome engineering and accelerated evolution
Wang, H. H., Isaacs, F. J., Carr, P. A., Sun, Z. Z., Xu, G., Forest, C. R., & Church, G. M. (2009) · Nature · doi:10.1038/nature08187 · Cited by 1,529
Engineering Yeast Transcription Machinery for Improved Ethanol Tolerance and Production
Alper, H., Moxley, J., Nevoigt, E., Fink, G. R., & Stephanopoulos, G. (2006) · Science · doi:10.1126/science.1131969 · Cited by 823
Cited by
Engineering Cellular Metabolism
Nielsen, J., & Keasling, J. D. (2016) · Cell · doi:10.1016/j.cell.2016.02.004 · Cited by 1,378
Self-citationPrinciples of genetic circuit design
Brophy, J. A. N., & Voigt, C. A. (2014) · Nature Methods · doi:10.1038/nmeth.2926 · Cited by 931
Engineering dynamic pathway regulation using stress-response promoters
Dahl, R. H., Zhang, F., Alonso-Gutierrez, J., Baidoo, E., Batth, T. S., Redding-Johanson, A. M., Petzold, C. J., Mukhopadhyay, A., Lee, T. S., Adams, P. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2689 · Cited by 473
Self-citationEngineering an allosteric transcription factor to respond to new ligands
Taylor, N. D., Garruss, A. S., Moretti, R., Chan, S., Arbing, M. A., Cascio, D., Rogers, J. K., Isaacs, F. J., Kosuri, S., Baker, D., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3696 · Cited by 328
Evolution-guided optimization of biosynthetic pathways
Raman, S., Rogers, J. K., Taylor, N. D., & Church, G. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1409523111 · Cited by 278
Metadata
- Authors
- Dietrich, J. A., Shis, D. L., Alikhani, A., & Keasling, J. D.
- Venue
- ACS Synthetic Biology
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-11-14
- Volume
- 2
- Issue
- 1
- Pages
- 47-58
- ISSN
- 2161-5063 (ISSN-L)
- OpenAlex
- W2081799587
- Cited by
- 206
- References
- 4
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Transcription Factor-Based Screens and Synthetic Selections for Microbial Small-Molecule Biosynthesis. https://app.cochat.ai/papers/10.1021/sb300091d
- The paper
- Dietrich, J. A., Shis, D. L., Alikhani, A., & Keasling, J. D. (2013). Transcription Factor-Based Screens and Synthetic Selections for Microbial Small-Molecule Biosynthesis. ACS Synthetic Biology. https://doi.org/10.1021/sb300091d
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.