Evidence map›Paper›PMID 38031061›Full record

ArticleMicrobial cell factories2023

Improved production of Taxol

Koray Malcı, Rodrigo Santibáñez, Nestor Jonguitud-Borrego, Jorge H Santoyo-Garcia, Eduard J Kerkhoven, Leonardo Rios-Solis

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

11 citing papers in PubMed, 26 citations in OpenAlex.

  1. Review
  2. Article
  3. Introduction of human mMicrobial cell factories · 2025
    Article
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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

6 authors at 3 institutions in 4 countries.

Koray MalcıInstitute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh, EH9 3BF, UK. k.malci@imperial.ac.uk.
Rodrigo SantibáñezDepartment of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0760, USA.
Nestor Jonguitud-BorregoInstitute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh, EH9 3BF, UK.
Jorge H Santoyo-GarciaInstitute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh, EH9 3BF, UK.
Eduard J KerkhovenDepartment of Life Sciences, Chalmers University of Technology, Kemivägen 10, SE-412 96, Gothenburg, Sweden.
Leonardo Rios-SolisInstitute for Bioengineering, School of Engineering, University of Edinburgh, King's Buildings, Edinburgh, EH9 3BF, UK. leo.rios@ucl.ac.uk.
University of Edinburgh · GBChalmers University of Technology · SEUniversity of California, San Diego · US

Funding

British Council 527429894Engineering and Physical Sciences Research Council Grant number EP/R513209/1Mexican National Council for Science and Technology CVU: 537962Mexican National Council for Science and Technology CVU:675492Ministry of National Education of Turkey YLSY programNovo Nordisk Foundation NNF20CC0035580Royal Society RSG\R1\180345
6 · The paper itself

Abstract

backgroundIntegrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol

resultsUsing constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol

conclusionsThis study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol

Indexed as

DiterpenesPaclitaxelAlkenesMetabolic EngineeringSaccharomyces cerevisiaeAlkenesDiterpenesPaclitaxeltaxa-4(5),11(12)dieneComputational metabolic engineeringGenome-scale modellingin silico designMevalonate pathwaySaccharomyces cerevisiaeSynthetic biologySystems biologyTaxadieneTaxol

Identifiers

PMID38031061
PMCPMC10687855
OpenAlexW4389134264

What OpenQuestion holds

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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.