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Mendeley readers
Attention Score in Context
| Title |
Systems-based approaches enable identification of gene targets which improve the flavour profile of low-ethanol wine yeast strains
|
|---|---|
| Published in |
Metabolic Engineering, August 2018
|
| DOI | 10.1016/j.ymben.2018.08.006 |
| Pubmed ID | |
| Authors |
Cristian Varela, Simon A. Schmidt, Anthony R. Borneman, Chi Nam Ignatius Pang, Jens O. Krömerx, Alamgir Khan, Xiaomin Song, Mark P. Hodson, Mark Solomon, Christine M. Mayr, Wade Hines, Isak S. Pretorius, Mark S. Baker, Ute Roessner, Meagan Mercurio, Paul A. Henschke, Marc R. Wilkins, Paul J. Chambers |
| Abstract |
Metabolic engineering has been vital to the development of industrial microbes such as the yeast Saccharomyces cerevisiae. However, sequential rounds of modification are often needed to achieve particular industrial design targets. Systems biology approaches can aid in identifying genetic targets for modification through providing an integrated view of cellular physiology. Recently, research into the generation of commercial yeasts that can produce reduced-ethanol wines has resulted in metabolically-engineered strains of S. cerevisiae that are less efficient at producing ethanol from sugar. However, these modifications led to the concomitant production of off-flavour by-products. A combination of transcriptomics, proteomics and metabolomics was therefore used to investigate the physiological changes occurring in an engineered low-ethanol yeast strain during alcoholic fermentation. Integration of 'omics data identified several metabolic reactions, including those related to the pyruvate node and redox homeostasis, as being significantly affected by the low-ethanol engineering methodology, and highlighted acetaldehyde and 2,4,5-trimethyl-1,3-dioxolane as the main off-flavour compounds. Gene remediation strategies were then successfully applied to decrease the formation of these by-products, while maintaining the 'low-alcohol' phenotype. The data generated from this comprehensive systems-based study will inform wine yeast strain development programmes, which, in turn, could potentially play an important role in assisting winemakers in their endeavour to produce low-alcohol wines with desirable flavour profiles. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 22% |
| Student > Ph. D. Student | 14 | 19% |
| Student > Master | 7 | 9% |
| Student > Bachelor | 7 | 9% |
| Student > Postgraduate | 5 | 7% |
| Other | 12 | 16% |
| Unknown | 13 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 36% |
| Biochemistry, Genetics and Molecular Biology | 15 | 20% |
| Engineering | 5 | 7% |
| Chemical Engineering | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 3 | 4% |
| Unknown | 20 | 27% |
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 14 September 2018.
All research outputs
#20,663,600
of 25,385,509 outputs
Outputs from Metabolic Engineering
#1,305
of 1,463 outputs
Outputs of similar age
#265,934
of 341,989 outputs
Outputs of similar age from Metabolic Engineering
#30
of 35 outputs
Altmetric has tracked 25,385,509 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.
So far Altmetric has tracked 1,463 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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 341,989 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.