Computational Predictions Provide Insights into the Biology of TAL Effector Target Sites

Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library Jstacs, and is freely available as a web-application and a command line program.转录激活因子样（Transcription activator-like, TAL）效应物是黄单胞菌（Xanthomonas）注入宿主植物细胞的效应蛋白，可作为转录激活因子发挥功能，以利于病原菌侵染。TAL效应物的DNA结合域由保守的氨基酸重复结构构成，其中包含决定DNA结合特异性的重复可变双残基（repeat-variable diresidues, RVDs）。本文提出TALgetter这一新方法，用于基于统计模型预测TAL效应物的靶位点。与既往研究方法不同，TALgetter的参数可通过训练数据经计算获得。我们证实，相较于现有方法——TALE-NT工具集的Target Finder，TALgetter能够成功预测已知的TAL效应物靶位点，且通常可得到更多与基因表达微阵列中基因上调结果一致的预测结果。我们对TALgetter所估计的结合特异性开展了研究，证实不同的RVDs对转录激活的重要性存在差异。后续研究表明，TALgetter的预测结果揭示了TAL效应物靶位点相对于转录起始位点的一种此前未被报道的位置偏好性。此外，我们预测有若干TAL效应物可结合TATA盒，这或许代表了TAL效应物介导转录激活的一种通用模式。通过对TALgetter的预测靶位点进行分析，我们提出了水稻和甜橙中数个新型TAL效应物致病靶标。基因表达微阵列数据支持这些候选靶标受到TAL介导的诱导。这些靶标的有效性还可通过以下证据得到证实：与已知TAL效应物靶标存在功能类比、具有相似功能的TAL效应物靶标过度富集，或是其生物学功能与病原菌侵染相关。因此，这些预测得到的TAL效应物致病靶标是研究TAL效应物致病功能的潜在优质候选对象。TALgetter已作为开源Java库Jstacs的一部分实现，并可作为Web应用程序与命令行程序免费获取。