Evidence map›Paper›PMID 26070803›Full record

ArticleMicrobial cell factories2015

Determination of key enzymes for threonine synthesis through in vitro metabolic pathway analysis.

Yanfei Zhang, Qinglong Meng, Hongwu Ma, Yongfei Liu, Guoqiang Cao, Xiaoran Zhang, Ping Zheng, Jibin Sun, Dawei Zhang, Wenxia Jiang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Microbial cell factories, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

12 citing papers in PubMed, 38 citations in OpenAlex.

  1. Article
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  6. Article
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  8. Cell-free biosynthesis of limonene using enzyme-enrichedSynthetic biology (Oxford, England) · 2019
    Article
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  10. Review
  11. 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

11 authors at 2 institutions in 1 country.

Yanfei ZhangKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. zhang_yf1@tib.cas.cn.
Qinglong MengKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. mql198868@tju.edu.cn.
Hongwu MaKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. ma_hw@tib.cas.cn.
Yongfei LiuKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. liu_yf@tib.cas.cn.
Guoqiang CaoKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. cao_gq@tib.cas.cn.
Xiaoran ZhangKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. zhang_xr@tib.cas.cn.
Ping ZhengKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. zheng_p@tib.cas.cn.
Jibin SunKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. sun_jb@tib.cas.cn.
Dawei ZhangKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. zhang_dw@tib.cas.cn.
Wenxia JiangKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. jiang_wx@tib.cas.cn.
Yanhe MaKey Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China. ma_yh@tib.cas.cn.
Tianjin Institute of Industrial Biotechnology · CNChinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe overexpression of key enzymes in a metabolic pathway is a frequently used genetic engineering strategy for strain improvement. Metabolic control analysis has been proposed to quantitatively determine key enzymes. However, the lack of quality data often makes it difficult to correctly identify key enzymes through control analysis. Here, we proposed a method combining in vitro metabolic pathway analysis and proteomics measurement to find the key enzymes in threonine synthesis pathway.

resultsAll enzymes in the threonine synthesis pathway were purified for the reconstruction and perturbation of the in vitro pathway. Label-free proteomics technology combined with APEX (absolute protein expression measurements) data analysis method were employed to determine the absolute enzyme concentrations in the crude enzyme extract obtained from a threonine production strain during the fastest threonine production period. The flux control coefficient of each enzyme in the pathway was then calculated by measuring the flux changes after titration of the corresponding enzyme. The isoenzyme LysC catalyzing the first step in the pathway has the largest flux control coefficient, and thus its concentration change has the biggest impact on pathway flux. To verify that the key enzyme identified through in vitro pathway analysis is also the key enzyme in vivo, we overexpressed LysC in the original threonine production strain. Fermentation results showed that the threonine concentration was increased 30% and the yield was increased 20%.

conclusionsIn vitro metabolic pathways simulating in vivo cells can be built based on precise measurement of enzyme concentrations through proteomics technology and used for the determination of key enzymes through metabolic control analysis. This provides a new way to find gene overexpression targets for industrial strain improvement.

Indexed as

Biosynthetic PathwaysEscherichia coliEscherichia coli ProteinsKineticsThreonineEscherichia coli ProteinsThreonine

Identifiers

PMID26070803
PMCPMC4465468
OpenAlexW1481616865

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.