GmMYB61 and GmWRKY2 AmpDAP-seq binding site data.

Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.大豆疫霉（Phytophthora sojae）是一类土传卵菌，亦是引发大豆（Glycine max [L.] Merrill）疫霉根腐病（PRR）的病原物。在发病适宜环境中，该菌导致的产量损失极具毁灭性，全球年度估算损失超110万吨。长期以来，疫霉根腐病的防控策略以寄主遗传抗性（包括垂直抗性与水平抗性）为核心，辅以抑病栽培措施（如卵菌灭杀剂施用）。但随着复杂多样的大豆疫霉菌致病型快速扩散，亟需开发新技术以缓解田间疫霉根腐病的发生。为此，本研究旨在结合高通量测序数据与深度学习技术，阐明大豆感染大豆疫霉后的分子特征。 为此，我们构建了转录组，以鉴定大豆与大豆疫霉发生亲和、非亲和互作及模拟接种条件下的差异表达基因（DEGs）。随后利用该表达数据筛选出两个隶属于WRKY和RAV家族的防御相关转录因子（TFs）。针对每个转录因子获取了DNA亲和纯化测序（DAP-seq）数据，以此获得大豆基因组中的推定DNA结合位点。利用这些结合位点训练带有卷积与循环层的深度神经网络，以预测差异表达基因集中WRKY和RAV家族成员的新型靶位点。此外，我们利用公开可用的拟南芥（Arabidopsis thaliana）DAP-seq数据，针对本研究转录组分析中富集的5个转录因子家族构建类似模型。这些基于拟南芥数据的模型被用于在大豆中开展跨物种转录因子结合位点预测。最终，我们构建了基因调控网络，用以描述调控大豆抗大豆疫霉免疫应答的转录因子-靶基因互作关系。 本研究结果为植物-病原物分子互作研究提供了全新视角，同时有望助力培育对大豆疫霉具有更持久抗性的大豆品种。