Charting Shifts in Saccharomyces cerevisiae Gene Expression across Asynchronous Time Trajectories with Diffusion Maps.
Charting Shifts in Saccharomyces cerevisiae Gene Expression across Asynchronous Time Trajectories with Diffusion Maps.
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DOI：
10.1128/mbio.02345-21
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发表时间：
2021-10-26
期刊：
影响因子：
6.4
通讯作者：
Montpetit B
中科院分区：
文献类型：
--
作者：
Reiter T;Montpetit R;Runnebaum R;Brown CT;Montpetit B
在发酵过程中，酿酒酵母代谢糖和其他营养物质以获得生长和存活所需的能量，同时还调节这些活动以响应细胞-环境相互作用。这里，S.在发酵的时间过程中探索酿酒酵母基因表达，并用于区分发酵，使用来自15个独特位点的黑比诺葡萄。发酵以不同速率进行的事实使数据分析复杂化，使得难以用常规差异表达工具直接比较时间序列基因表达数据。这导致了一种新的方法相结合的扩散映射与连续差分表达分析（称为DMap-DE）的发展。使用这种方法，在基因表达的位点特异性偏差进行了鉴定，包括与非酵母酵母Hanseniaspora uvarum，以及初始氮浓度的葡萄必须在基因表达的变化。这些结果突出了位点特异性变量与酿酒酵母基因表达之间的新关系，这些基因表达与重复发酵结果有关。还证明了DMap-DE可以从其他背景（例如，缺氧反应的酿酒酵母），并提供优于其他数据降维方法，表明DMap-DE提供了一个强大的方法，研究异步时间序列基因表达数据。
During fermentation, Saccharomyces cerevisiae metabolizes sugars and other nutrients to obtain energy for growth and survival, while also modulating these activities in response to cell-environment interactions. Here, differences in S. cerevisiae gene expression were explored over a time course of fermentation and used to differentiate fermentations, using Pinot noir grapes from 15 unique sites. Data analysis was complicated by the fact that the fermentations proceeded at different rates, making a direct comparison of time series gene expression data difficult with conventional differential expression tools. This led to the development of a novel approach combining diffusion mapping with continuous differential expression analysis (termed DMap-DE). Using this method, site-specific deviations in gene expression were identified, including changes in gene expression correlated with the non-Saccharomyces yeast Hanseniaspora uvarum, as well as initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated fermentation outcomes. It was also demonstrated that DMap-DE can extract biologically relevant gene expression patterns from other contexts (e.g., hypoxic response of Saccharomyces cerevisiae) and offers advantages over other data dimensionality reduction approaches, indicating that DMap-DE offers a robust method for investigating asynchronous time series gene expression data.