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Metabolome- and genome-scale model analyses for engineering of Aureobasidium pullulans to enhance polymalic acid and malic acid production from sugarcane molasses

Overview of attention for article published in Biotechnology for Biofuels and Bioproducts, April 2018
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  • High Attention Score compared to outputs of the same age and source (80th percentile)

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1 blog

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44 Mendeley
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Title
Metabolome- and genome-scale model analyses for engineering of Aureobasidium pullulans to enhance polymalic acid and malic acid production from sugarcane molasses
Published in
Biotechnology for Biofuels and Bioproducts, April 2018
DOI 10.1186/s13068-018-1099-7
Pubmed ID
Authors

Jun Feng, Jing Yang, Wenwen Yang, Jie Chen, Min Jiang, Xiang Zou

Abstract

Polymalic acid (PMA) is a water-soluble biopolymer with many attractive properties for food and pharmaceutical applications mainly produced by the yeast-like fungus Aureobasidium pullulans. Acid hydrolysis of PMA, resulting in release of the monomer l-malic acid (MA), which is widely used in the food and chemical industry, is a competitive process for producing bio-based platform chemicals. In this study, the production of PMA and MA from sucrose and sugarcane molasses by A. pullulans was studied in shake flasks and bioreactors. Comparative metabolome analysis of sucrose- and glucose-based fermentation identified 81 intracellular metabolites and demonstrated that pyruvate from the glycolysis pathway may be a key metabolite affecting PMA synthesis. In silico simulation of a genome-scale metabolic model (iZX637) further verified that pyruvate carboxylase (pyc) via the reductive tricarboxylic acid cycle strengthened carbon flux for PMA synthesis. Therefore, an engineered strain, FJ-PYC, was constructed by overexpressing the pyc gene, which increased the PMA titer by 15.1% compared with that from the wild-type strain in a 5-L stirred-tank fermentor. Sugarcane molasses can be used as an economical substrate without any pretreatment or nutrient supplementation. Using fed-batch fermentation of FJ-PYC, we obtained the highest PMA titers (81.5, 94.2 g/L of MA after hydrolysis) in 140 h with a corresponding MA yield of 0.62 g/g and productivity of 0.67 g/L h. We showed that integrated metabolome- and genome-scale model analyses were an effective approach for engineering the metabolic node for PMA synthesis, and also developed an economical and green process for PMA and MA production from renewable biomass feedstocks.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 18%
Student > Ph. D. Student 6 14%
Researcher 5 11%
Student > Bachelor 4 9%
Student > Doctoral Student 3 7%
Other 7 16%
Unknown 11 25%
Readers by discipline Count As %
Engineering 9 20%
Biochemistry, Genetics and Molecular Biology 8 18%
Chemical Engineering 6 14%
Agricultural and Biological Sciences 6 14%
Business, Management and Accounting 1 2%
Other 3 7%
Unknown 11 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 April 2018.
All research outputs
#6,600,606
of 25,382,440 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#397
of 1,578 outputs
Outputs of similar age
#107,114
of 342,873 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#9
of 50 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,578 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 74% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 342,873 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.