Construction of transcript regulation mechanism prediction models based on binding motif environment of transcription factor AoXlnR in Aspergillus oryzae.
Construction of transcript regulation mechanism prediction models based on binding motif environment of transcription factor AoXlnR in Aspergillus oryzae.
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基于米曲霉转录因子AoXlnR结合基序环境的转录调控机制预测模型构建
DOI：
10.1101/2021.07.28.454268
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
H.
中科院分区：
文献类型：
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作者：
Oka;H.;Kojima;T.;Kato;R.;Ihara;K.;and Nakano;H.
最近的研究表明，尽管在启动子区域存在一个或多个AoXlnR结合基序，但仍有数千个基因不受AoXlnR表达增加的影响。鉴于这一点，我们设计了这项研究，以构建几个预测模型，以确定一个基因是否可以对AoXlnR表达的变化表现出差异反应。这些模型是使用3D DNA形状信息构建的，该信息使用AoXlnR结合基序周围的序列确定，并将其归类为功能或非功能。这些模型是使用支持向量机创建的，然后进行评估，以确定这些基于DNA形状的模型是否可以根据接收器工作特性曲线下的面积正确地分类功能基序。结果表明，AoXlnR基序下游基因的差异表达水平与结合基序周围特定的DNA形状信息密切相关。此外，通过比较只有一个结合DNA基序的区域和具有多个结合DNA基序的区域的预测模型，我们发现导致差异表达的参数根据启动子区域中基序的数量而有所不同。作者摘要DNA结合转录因子在转录调控机制中发挥核心作用，主要是通过它们与基因组上的靶点的特异性结合和对下游基因表达的调控。因此，全面分析这些转录因子的功能将有助于理解各种生物学机制。然而，TFsin的活体功能是多样和复杂的，基因组上已识别的结合部位并不一定参与下游基因表达的调控。在这项研究中，我们研究了转录因子结合位点周围的DNA结构信息是否可以用来预测结合位点参与调控位于结合位点下游的基因的表达。具体地说，我们根据位于基因上游的DNA结合基序周围的DNA形状计算了结构参数，该基序的表达直接受到米曲霉转录因子AoXlnR的调控，并表明通过结合这些参数的机器学习可以根据序列信息高精度地预测表达调控的存在或不存在。
Recent study revealed that there are thousands of genes that remain unaffected by increased AoXlnR expression, despite the presence of one or more AoXlnR-binding motifs in their promoter region. Given this knowledge, we designed this study to construct several predictive models for determining whether a gene can exhibit a differential response to changes in AoXlnR expression. These models were constructed using 3D DNA shape information determined using the sequence around the AoXlnR binding motifs with classification as functional or nonfunctional. These models were created using a support vector machine followed by the evaluations designed to determine whether these DNA shape-based models can correctly classify functional motifs in terms of area under the receiver operating characteristic curve. The results showed that the differential expression levels of genes located downstream of the AoXlnR motif are closely related to specific DNA shape information around the binding motifs. Furthermore, we found that the parameters contributing to differential expressions differed depending on the number of motifs in the promoter region by comparing the prediction models using regions with only one binding DNA motif and those with multiple binding DNA motifs.Author SummaryDNA-binding transcription factors (TFs) play a central role in transcriptional regulation mechanisms, mainly through their specific binding to target sites on the genome and regulation of the expression of downstream genes. Therefore, a comprehensive analysis of the function of these TFs will lead to the understanding of various biological mechanisms. However, the functions of TFsin vivoare diverse and complicated, and the identified binding sites on the genome are not necessarily involved in the regulation of downstream gene expression. In this study, we investigated whether DNA structural information around the binding site of transcription factors can be used to predict the involvement of the binding site in the regulation of the expression of genes located downstream of the binding site. Specifically, we calculated the structural parameters based on the DNA shape around the DNA binding motif located upstream of the gene whose expression is directly regulated by the transcription factor AoXlnR fromAspergillus oryzae, and showed that the presence or absence of expression regulation can be predicted from the sequence information with high accuracy by machine learning incorporating these parameters.