Computational models for prediction of yeast strain potential for winemaking from phenotypic profiles.
Computational models for prediction of yeast strain potential for winemaking from phenotypic profiles.
复制标题
DOI：
10.1371/journal.pone.0066523
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Schuller D
中科院分区：
文献类型：
--
作者：
Mendes I;Franco-Duarte R;Umek L;Fonseca E;Drumonde-Neves J;Dequin S;Zupan B;Schuller D
来自不同自然栖息地的酿酒酵母菌株具有大量的表型多样性，这是由酵母和各自环境之间的相互作用驱动的。在葡萄汁发酵过程中，菌株暴露于广泛的生物和非生物应激源，这可能导致菌株选择并产生自然产生的菌株多样性。某些表型对酿酒工业特别感兴趣，可以通过筛选大量不同的菌株来鉴定。目前工作的目的是使用数据挖掘方法来确定那些表型测试，是最有用的预测菌株的酿酒潜力。我们已经建立了一个由172个全球地理起源或技术应用菌株组成的酿酒葡萄球菌集合。他们的表型是通过考虑30生理性状，从酿酒的角度来看是重要的筛选。主成分分析表明，在亚硫酸氢钾存在下的生长、在40°C下的生长以及对乙醇的抗性是造成菌株变异的主要原因。在表型谱的分层聚类中，来自相同葡萄酒和葡萄园的菌株分散在所有聚类中，而商业酿酒菌株倾向于共聚类。曼-惠特尼检验结果显示，表型结果与菌株的技术应用或来源有显著相关性。Naïve贝叶斯分类器从30个表型试验中鉴定出3个在异丙二醇（0.05 mg/mL）、环己亚胺（0.1µg/mL）和亚硫酸氢钾（150 mg/mL）中生长的表型试验，为将菌株分配到商业菌株组提供了最多的信息。使用整个表型谱，将菌株分配给该组的概率为27%，当仅考虑三个测试的结果时，该概率增加到95%。结果表明，计算方法可以简化应变选择过程。
Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40°C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.
登录
查看更多内容
影响因子：
5.8
作者：
Curk, T;Demsar, J;Zupan, B
通讯作者：
Zupan, B
影响因子：
5.1
作者：
Goddard, Matthew R.;Anfang, Nicole;Jun, Casey
通讯作者：
Jun, Casey
影响因子：
2.6
作者：
Franco-Duarte, R.;Umek, L.;Schuller, D.
通讯作者：
Schuller, D.
影响因子：
4.9
作者：
Muller, Ludo A. H.;Mccusker, John H.
通讯作者：
Mccusker, John H.
影响因子：
3.7
作者：
Camarasa C;Sanchez I;Brial P;Bigey F;Dequin S
通讯作者：
Dequin S