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Employing a combinatorial expression approach to characterize xylose utilization in Saccharomyces cerevisiae

Overview of attention for article published in Metabolic Engineering, June 2014
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Title
Employing a combinatorial expression approach to characterize xylose utilization in Saccharomyces cerevisiae
Published in
Metabolic Engineering, June 2014
DOI 10.1016/j.ymben.2014.06.002
Pubmed ID
Authors

Luke N. Latimer, Michael E. Lee, Daniel Medina-Cleghorn, Rebecca A. Kohnz, Daniel K. Nomura, John E. Dueber

Abstract

Fermentation of xylose, a major constituent of lignocellulose, will be important for expanding sustainable biofuel production. We sought to better understand the effects of intrinsic (genotypic) and extrinsic (growth conditions) variables on optimal gene expression of the Scheffersomyces stipitis xylose utilization pathway in Saccharomyces cerevisiae by using a set of five promoters to simultaneously regulate each gene. Three-gene (xylose reductase, xylitol dehydrogenase (XDH), and xylulokinase) and eight-gene (expanded with non-oxidative pentose phosphate pathway enzymes and pyruvate kinase) promoter libraries were enriched under aerobic and anaerobic conditions or with a mutant XDH with altered cofactor usage. Through characterization of enriched strains, we observed 1) differences in promoter enrichment for the three-gene library depending on whether the pentose phosphate pathway genes were included during the aerobic enrichment; 2) the importance of selection conditions, where some aerobically-enriched strains underperform in anaerobic conditions compared to anaerobically-enriched strains; 3) improved growth rather than improved fermentation product yields for optimized strains carrying the mutant XDH compared to the wild-type XDH.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
China 1 <1%
Belgium 1 <1%
Brazil 1 <1%
Unknown 124 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 25%
Student > Ph. D. Student 27 21%
Student > Master 15 12%
Student > Bachelor 8 6%
Student > Doctoral Student 6 5%
Other 17 13%
Unknown 24 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 32%
Biochemistry, Genetics and Molecular Biology 33 26%
Engineering 9 7%
Chemistry 8 6%
Chemical Engineering 7 5%
Other 4 3%
Unknown 27 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 15 May 2015.
All research outputs
#20,655,488
of 25,371,288 outputs
Outputs from Metabolic Engineering
#1,305
of 1,463 outputs
Outputs of similar age
#178,190
of 243,417 outputs
Outputs of similar age from Metabolic Engineering
#16
of 20 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.