The transcriptional network activated by Cln3 cyclin at the G1-to-S transition of the yeast cell cycle.
The transcriptional network activated by Cln3 cyclin at the G1-to-S transition of the yeast cell cycle.
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DOI：
10.1186/gb-2010-11-6-r67
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发表时间：
2010
期刊：
影响因子：
12.3
通讯作者：
Aldea M
中科院分区：
文献类型：
--
作者：
Ferrezuelo F;Colomina N;Futcher B;Aldea M
酿酒酵母（Saccharomyces cerevisiae）细胞周期的G1-S转变涉及由转录因子SBF（Swi 4-Swi 6）和MBF（Mbp 1-Swi 6）驱动的广泛的转录程序。这些因子的激活最终取决于G1期细胞周期蛋白Cln 3。为了确定Cln 3的转录靶点及其对SBF或MBF的依赖性，我们首先使用DNA微阵列来询问缺乏SBF和/或MBF组分的菌株的同步培养物中Cln 3过表达后的基因表达。其次，我们将这个表达数据集与其他异构数据源集成到一个基于贝叶斯统计的概率模型中。我们的分析已经产生了200多个转录因子的目标分配，通过ChIP检测和功能富集验证。我们的预测显示出比以前的分类更高的内部一致性和预测能力。我们的研究结果支持一个模型，即SBF和MBF可能被Cln 3差异激活。整合异构的全基因组数据集是构建精确转录网络的关键。通过这样的整合，我们在这里提供了一个可靠的转录网络在G1-到-S过渡的芽殖酵母细胞周期。我们的结果表明，为了提高预测的可靠性，我们需要为我们的模型提供更多信息丰富的实验数据。
The G1-to-S transition of the cell cycle in the yeast Saccharomyces cerevisiae involves an extensive transcriptional program driven by transcription factors SBF (Swi4-Swi6) and MBF (Mbp1-Swi6). Activation of these factors ultimately depends on the G1 cyclin Cln3. To determine the transcriptional targets of Cln3 and their dependence on SBF or MBF, we first have used DNA microarrays to interrogate gene expression upon Cln3 overexpression in synchronized cultures of strains lacking components of SBF and/or MBF. Secondly, we have integrated this expression dataset together with other heterogeneous data sources into a single probabilistic model based on Bayesian statistics. Our analysis has produced more than 200 transcription factor-target assignments, validated by ChIP assays and by functional enrichment. Our predictions show higher internal coherence and predictive power than previous classifications. Our results support a model whereby SBF and MBF may be differentially activated by Cln3. Integration of heterogeneous genome-wide datasets is key to building accurate transcriptional networks. By such integration, we provide here a reliable transcriptional network at the G1-to-S transition in the budding yeast cell cycle. Our results suggest that to improve the reliability of predictions we need to feed our models with more informative experimental data.
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