Evidence map›Paper›PMID 23705993›Full record

ArticleMicrobial cell factories2013

Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement.

Di Huang, Shanshan Li, Menglei Xia, Jianping Wen, Xiaoqiang Jia

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

27 citing papers in PubMed, 81 citations in OpenAlex.

  1. Article
  2. Review
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  6. Optimization of the precursor supply for an enhanced FK506 production inFrontiers in bioengineering and biotechnology · 2022
    Article
  7. Metabolic engineering of3 Biotech · 2021
    Article
  8. Article
  9. Applied biochemistry and microbiology · 2021
    Article
  10. The Onset of Tacrolimus Biosynthesis inAntibiotics (Basel, Switzerland) · 2020
    Article
  11. Article
  12. Review
  13. 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

5 authors at 1 institution in 1 country.

Di HuangDepartment of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, PR China.
Shanshan Li
Menglei Xia
Jianping Wen
Xiaoqiang Jia
Tianjin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis.

resultsFirst, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L).

conclusionsResults confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites.

Indexed as

Genome, BacterialBatch Cell Culture TechniquesGene Knockout TechniquesMetabolic EngineeringMetabolic Networks and PathwaysMetabolomeMultigene FamilyStreptomycesTacrolimusTacrolimus

Identifiers

PMID23705993
PMCPMC3680238
OpenAlexW2126731835

What OpenQuestion holds

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LicenceCC BY
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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.