Computational analysis of GAL pathway pinpoints mechanisms underlying natural variation.
Computational analysis of GAL pathway pinpoints mechanisms underlying natural variation.
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GAL途径的计算分析精确定位了自然变异的机制。
DOI：
10.1371/journal.pcbi.1008691
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发表时间：
2021-09
影响因子：
4.3
通讯作者：
Springer M
中科院分区：
文献类型：
--
作者：
Hong J;Palme J;Hua B;Springer M
数量性状是可测量的表型，在很宽的表型范围内显示出连续的变异。近年来，研究者们在确定遗传因素对各种数量性状的影响方面付出了巨大的努力，但取得的成果参差不齐。我们在一个易于处理的模型系统中确定了一个数量性状，即酵母中的GAL途径，该途径控制糖半乳糖的摄取和代谢。GAL途径活化取决于半乳糖浓度和竞争性的优选糖（如葡萄糖）的浓度。天然酵母分离株在该途径的行为中显示出实质性变化。所有研究的酵母菌株表现出双峰响应相对于外部半乳糖浓度，即一组半乳糖浓度存在的GAL诱导和GAL抑制的亚群观察。然而，这些浓度在不同菌株中不同。我们建立了GAL途径的机制模型，并确定了用于捕获一组菌株（包括标准实验室菌株、天然变体和突变体）的表型特征的合理候选参数。这些参数的计算机扰动确定了细胞内半乳糖传感器Gal3p、GAL调控网络Gal80p内的负反馈节点和己糖转运蛋白HXT的变化，作为双峰范围变化的主要来源。我们能够通过调整与这三个元素相关的参数，在计算机中切换单个酵母菌株的表型。确定这些行为差异的基础可以深入了解GAL途径如何处理信息，以及不同菌株中营养代谢偏好的演变。更一般地说，我们的方法，确定的关键参数，解释在这个系统中的表型变异应普遍适用于其他数量性状。微生物采取精心设计的策略，优先吸收和使用营养物质，以科普复杂和波动的环境。因此，来自不同生态位的酵母菌株在响应营养信号诱导半乳糖代谢（GAL）途径中的基因的方式上显示出显着的变化。为了确定这种变化的机制来源，我们建立了一个数学模型来模拟半乳糖代谢调节网络的动力学，并研究了具有不同生物学意义的参数如何对自然变化做出贡献。我们发现半乳糖传感器Gal3p、负反馈节点Gal80p和己糖转运蛋白HXT的行为变化是GAL途径响应中的关键要素。在计算机中调整单个参数足以实现不同酵母菌株之间的表型转换。我们的计算方法应该是普遍有用的，以帮助查明在其他系统中的自然变异的遗传和分子基础。
Quantitative traits are measurable phenotypes that show continuous variation over a wide phenotypic range. Enormous effort has recently been put into determining the genetic influences on a variety of quantitative traits with mixed success. We identified a quantitative trait in a tractable model system, the GAL pathway in yeast, which controls the uptake and metabolism of the sugar galactose. GAL pathway activation depends both on galactose concentration and on the concentrations of competing, preferred sugars such as glucose. Natural yeast isolates show substantial variation in the behavior of the pathway. All studied yeast strains exhibit bimodal responses relative to external galactose concentration, i.e. a set of galactose concentrations existed at which both GAL-induced and GAL-repressed subpopulations were observed. However, these concentrations differed in different strains. We built a mechanistic model of the GAL pathway and identified parameters that are plausible candidates for capturing the phenotypic features of a set of strains including standard lab strains, natural variants, and mutants. In silico perturbation of these parameters identified variation in the intracellular galactose sensor, Gal3p, the negative feedback node within the GAL regulatory network, Gal80p, and the hexose transporters, HXT, as the main sources of the bimodal range variation. We were able to switch the phenotype of individual yeast strains in silico by tuning parameters related to these three elements. Determining the basis for these behavioral differences may give insight into how the GAL pathway processes information, and into the evolution of nutrient metabolism preferences in different strains. More generally, our method of identifying the key parameters that explain phenotypic variation in this system should be generally applicable to other quantitative traits. Microbes adopt elaborate strategies for the preferred uptake and use of nutrients to cope with complex and fluctuating environments. As a result, yeast strains originating from different ecological niches show significant variation in the way they induce genes in the galactose metabolism (GAL) pathway in response to nutrient signals. To identify the mechanistic sources of this variation, we built a mathematical model to simulate the dynamics of the galactose metabolic regulation network, and studied how parameters with different biological implications contributed to the natural variation. We found that variations in the behavior of the galactose sensor Gal3p, the negative feedback node Gal80p, and the hexose transporters HXT were critical elements in the GAL pathway response. Tuning single parameters in silico was sufficient to achieve phenotype switching between different yeast strains. Our computational approach should be generally useful to help pinpoint the genetic and molecular bases of natural variation in other systems.
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