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[ subject:"Agriculture." ]
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Understanding Wine Yeast Strain Vari...
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Ozcan, Ayca.
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Understanding Wine Yeast Strain Variation Using a Combined Lipidomic, Metabolomic and Transcriptomic Approach.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Understanding Wine Yeast Strain Variation Using a Combined Lipidomic, Metabolomic and Transcriptomic Approach./
作者:
Ozcan, Ayca.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
90 p.
附註:
Source: Masters Abstracts International, Volume: 81-04.
Contained By:
Masters Abstracts International81-04.
標題:
Agriculture. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13427636
ISBN:
9781085787581
Understanding Wine Yeast Strain Variation Using a Combined Lipidomic, Metabolomic and Transcriptomic Approach.
Ozcan, Ayca.
Understanding Wine Yeast Strain Variation Using a Combined Lipidomic, Metabolomic and Transcriptomic Approach.
- Ann Arbor : ProQuest Dissertations & Theses, 2019 - 90 p.
Source: Masters Abstracts International, Volume: 81-04.
Thesis (M.S.)--University of California, Davis, 2019.
This item must not be sold to any third party vendors.
Under identical fermentation conditions wine yeast (S. cerevisiae) strains demonstrate variation in maximum cell biomass formation. Yeast strains that yield higher biomass are found to be able to complete sugar depletion more efficiently than the lower biomass yielding strains. In order to understand this variation in nutrient utilization efficiency (NUE), we have taken a multifaceted approach to assess the metabolic and regulatory differences between yeast strains. In this study, four commercial wine yeast strains with varying NUE, Montrachet, Cote des Blanc, T306, and Uvaferm 43 were fermented in synthetic MMM medium under identical anaerobic fermentation conditions. The maximum cell concentration measured by absorbance at 600 nm was found to vary from 7.4 to 10.2 between analyzed strains. Intracellular and extracellular metabolites were analyzed using GC-MS and HPLC-RI, respectively. Complete analysis of phospholipid profile of each strain was performed using QqQ LC-MS. Moreover, a transcriptomics approach (RNA-Seq) was taken to understand relevant transcriptional control mechanisms. Partial Least Squares Regression used for metabolomic and lipidomic data shows that certain metabolic pathways including the pentose-phosphate pathway, TCA cycle, and fatty acid synthesis are the most relevant in determining NUE. Lipid profile analysis has shown that, while higher concentrations of Phosphotidylcholine (PC) and Phosphotidylethanolamine (PE) lipids in the yeast cell membrane are positively correlated with higher biomass yields, higher concentrations of Phosphotidylinositol (PI) lipids have the opposite relationship with biomass yield. Finally, analyzing gene expression levels using BINGO analysis identified genes related to cellular lipids, cofactors, and amino acid metabolic processes as being the most related to NUE.
ISBN: 9781085787581Subjects--Topical Terms:
518588
Agriculture.
Subjects--Index Terms:
Lipidomics
Understanding Wine Yeast Strain Variation Using a Combined Lipidomic, Metabolomic and Transcriptomic Approach.
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Under identical fermentation conditions wine yeast (S. cerevisiae) strains demonstrate variation in maximum cell biomass formation. Yeast strains that yield higher biomass are found to be able to complete sugar depletion more efficiently than the lower biomass yielding strains. In order to understand this variation in nutrient utilization efficiency (NUE), we have taken a multifaceted approach to assess the metabolic and regulatory differences between yeast strains. In this study, four commercial wine yeast strains with varying NUE, Montrachet, Cote des Blanc, T306, and Uvaferm 43 were fermented in synthetic MMM medium under identical anaerobic fermentation conditions. The maximum cell concentration measured by absorbance at 600 nm was found to vary from 7.4 to 10.2 between analyzed strains. Intracellular and extracellular metabolites were analyzed using GC-MS and HPLC-RI, respectively. Complete analysis of phospholipid profile of each strain was performed using QqQ LC-MS. Moreover, a transcriptomics approach (RNA-Seq) was taken to understand relevant transcriptional control mechanisms. Partial Least Squares Regression used for metabolomic and lipidomic data shows that certain metabolic pathways including the pentose-phosphate pathway, TCA cycle, and fatty acid synthesis are the most relevant in determining NUE. Lipid profile analysis has shown that, while higher concentrations of Phosphotidylcholine (PC) and Phosphotidylethanolamine (PE) lipids in the yeast cell membrane are positively correlated with higher biomass yields, higher concentrations of Phosphotidylinositol (PI) lipids have the opposite relationship with biomass yield. Finally, analyzing gene expression levels using BINGO analysis identified genes related to cellular lipids, cofactors, and amino acid metabolic processes as being the most related to NUE.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13427636
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