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首页> 外文期刊>Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation >Assembly of Two Transgenes in an Artificial Chromatin Domain Gives Highly Coordinated Expression in Tobacco
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Assembly of Two Transgenes in an Artificial Chromatin Domain Gives Highly Coordinated Expression in Tobacco

机译:人工染色质域中的两个转基因的组装在烟草中提供高度协调的表达。

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The chromatin loop model predicts that genes within the same chromatin domain exhibit coordinated regulation. We here present the first direct experimental support for this model in plants. Two reporter genes, the E. coli β-glucuronidase gene and the firefly luciferase gene, driven by different promoters, were placed between copies of the chicken lysozyme A element, a member of the matrix-associated region (MAR) group of chromatin boundary elements, and introduced in tobacco ( Nicotiana tabacum ). In plants carrying A elements, quantitative enzyme activities and mRNA levels of both genes show high correlations compared to control plants. The A element thus creates an artificial chromatin domain that yields coordinated expression. Surprisingly, enzyme activities correlated poorly with their respective mRNA levels. We hypothesize that this indicates the occurrence of “error pipelines” in data generation: systematic errors of a given analytical method will point in the same direction and cancel out in correlation analysis, resulting in better correlations. In combining different methods of analysis, however, such errors do not cancel out and as a result relevant correlations can be masked. Such error pipelines will have to be taken into account when different types of ( e.g. , whole-genome) data sets are combined in quantitative analyses.
机译:染色质环模型预测相同染色质域内的基因表现出协调的调控。我们在这里提出了该模型在植物中的第一个直接实验支持。在不同的启动子驱动下,两个报告基因E. coliβ-葡糖醛酸糖苷酶基因和萤火虫荧光素酶基因被放置在鸡溶菌酶A元件的拷贝之间,鸡溶菌酶A元件是染色质边界元件的基质相关区域(MAR)组的成员并引入烟草(Nicotiana tabacum)中。与对照植物相比,在携带A元素的植物中,两个基因的定量酶活性和mRNA水平均显示出高度相关性。因此,A元件可产生人工染色质域,从而产生协调的表达。出乎意料的是,酶活性与其各自的mRNA水平相关性很差。我们假设这表明在数据生成中出现了“错误流水线”:给定分析方法的系统错误将指向同一方向并在相关分析中抵消,从而产生更好的相关性。但是,在组合不同的分析方法时,此类错误不会消除,因此可以掩盖相关的相关性。当在定量分析中组合不同类型的(例如,全基因组)数据集时,必须考虑这样的错误流水线。

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