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A Novel Strategy to Construct Yeast Saccharomyces cerevisiae Strains for Very High Gravity Fermentation

Overview of attention for article published in PLOS ONE, February 2012
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
patent
1 patent

Citations

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73 Dimensions

Readers on

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116 Mendeley
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Title
A Novel Strategy to Construct Yeast Saccharomyces cerevisiae Strains for Very High Gravity Fermentation
Published in
PLOS ONE, February 2012
DOI 10.1371/journal.pone.0031235
Pubmed ID
Authors

Xianglin Tao, Daoqiong Zheng, Tianzhe Liu, Pinmei Wang, Wenpeng Zhao, Muyuan Zhu, Xinhang Jiang, Yuhua Zhao, Xuechang Wu

Abstract

Very high gravity (VHG) fermentation is aimed to considerably increase both the fermentation rate and the ethanol concentration, thereby reducing capital costs and the risk of bacterial contamination. This process results in critical issues, such as adverse stress factors (ie., osmotic pressure and ethanol inhibition) and high concentrations of metabolic byproducts which are difficult to overcome by a single breeding method. In the present paper, a novel strategy that combines metabolic engineering and genome shuffling to circumvent these limitations and improve the bioethanol production performance of Saccharomyces cerevisiae strains under VHG conditions was developed. First, in strain Z5, which performed better than other widely used industrial strains, the gene GPD2 encoding glycerol 3-phosphate dehydrogenase was deleted, resulting in a mutant (Z5ΔGPD2) with a lower glycerol yield and poor ethanol productivity. Second, strain Z5ΔGPD2 was subjected to three rounds of genome shuffling to improve its VHG fermentation performance, and the best performing strain SZ3-1 was obtained. Results showed that strain SZ3-1 not only produced less glycerol, but also increased the ethanol yield by up to 8% compared with the parent strain Z5. Further analysis suggested that the improved ethanol yield in strain SZ3-1 was mainly contributed by the enhanced ethanol tolerance of the strain. The differences in ethanol tolerance between strains Z5 and SZ3-1 were closely associated with the cell membrane fatty acid compositions and intracellular trehalose concentrations. Finally, genome rearrangements in the optimized strain were confirmed by karyotype analysis. Hence, a combination of genome shuffling and metabolic engineering is an efficient approach for the rapid improvement of yeast strains for desirable industrial phenotypes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Thailand 2 2%
Canada 1 <1%
Netherlands 1 <1%
Mexico 1 <1%
United States 1 <1%
Unknown 108 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 20%
Student > Bachelor 16 14%
Student > Doctoral Student 16 14%
Student > Master 14 12%
Researcher 13 11%
Other 18 16%
Unknown 16 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 53 46%
Biochemistry, Genetics and Molecular Biology 24 21%
Engineering 9 8%
Immunology and Microbiology 3 3%
Chemistry 3 3%
Other 5 4%
Unknown 19 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 08 January 2015.
All research outputs
#3,578,089
of 22,754,104 outputs
Outputs from PLOS ONE
#44,327
of 194,175 outputs
Outputs of similar age
#22,973
of 156,309 outputs
Outputs of similar age from PLOS ONE
#637
of 3,606 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 194,175 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done well, scoring higher than 77% 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 156,309 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 3,606 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.