Molecular Biosciences Graduate Program, Arkansas State University, State University, AR, United States of America.
Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America.
PLoS One. 2023 Jul 7;18(7):e0287590. doi: 10.1371/journal.pone.0287590. eCollection 2023.
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.
大豆疫霉是一种土壤传播的卵菌，是大豆疫霉根腐和茎腐（PRR）的病原体。在有利于发病的环境中，由大豆疫霉引起的产量损失是毁灭性的，全球估计每年超过 110 万吨。从历史上看，PRR 的管理涉及到宿主遗传抗性（垂直和水平），辅以抑制病害的文化措施（例如，施用杀真菌剂）。然而，复杂和/或多样化的大豆疫霉菌型的广泛扩张需要开发新技术来减轻田间环境中的 PRR。因此，本研究的目的是将高通量测序数据和深度学习相结合，阐明大豆感染大豆疫霉后的分子特征。为此，我们生成了转录组，以鉴定与大豆疫霉互作过程中差异表达的基因（DEGs），并与模拟接种进行比较。然后，我们使用表达数据选择两个属于 WRKY 和 RAV 家族的防御相关转录因子（TFs）。为每个 TF 获得了 DNA 亲和纯化和测序（DAP-seq）数据，提供了大豆基因组中潜在的 DNA 结合位点。这些结合位点被用于训练具有卷积和递归层的深度神经网络，以预测 DEG 集中 WRKY 和 RAV 家族成员的新靶位。此外，我们利用公开的拟南芥（Arabidopsis thaliana）DAP-seq 数据，对我们的转录组分析中富集的五个 TF 家族进行训练，以构建类似的模型。这些基于拟南芥数据的模型被用于在大豆上进行跨物种 TF 结合位点预测。最后，我们创建了一个基因调控网络，描述了 TF-靶基因相互作用，以协调对大豆疫霉的免疫反应。本文提供了关于植物-病原体分子互作的新见解，并可能有助于开发对大豆疫霉具有更持久抗性的大豆品种。
