Kuwada Eriko, Takeshita Kouki, Kawakatsu Taiji, Uchida Seiichi, Akagi Takashi
Graduate School of Environmental and Life Science, Okayama University, Okayama, 700-8530, Japan.
Department of Advanced Information Technology, Kyushu University, Fukuoka, 819-0395, Japan.
Plant J. 2024 Dec;120(5):1987-1999. doi: 10.1111/tpj.17093. Epub 2024 Oct 27.
Previous research on the ripening process of many fruit crop varieties typically involved analyses of the conserved genetic factors among species. However, even for seemingly identical ripening processes, the associated gene expression networks often evolved independently, as reflected by the diversity in the interactions between transcription factors (TFs) and the targeted cis-regulatory elements (CREs). In this study, explainable deep learning (DL) frameworks were used to predict expression patterns on the basis of CREs in promoter sequences. We initially screened potential lineage-specific CRE-TF interactions influencing the kiwifruit ripening process, which is triggered by ethylene, similar to the corresponding processes in other climacteric fruit crops. Some novel regulatory relationships affecting ethylene-induced fruit ripening were identified. Specifically, ABI5-like bZIP, G2-like, and MYB81-like TFs were revealed as trans-factors modulating the expression of representative ethylene signaling/biosynthesis-related genes (e.g., ACS1, ERT2, and ERF143). Transient reporter assays and DNA affinity purification sequencing (DAP-Seq) analyses validated these CRE-TF interactions and their regulatory relationships. A comparative analysis with co-expression networking suggested that this DL-based screening can identify regulatory networks independently of co-expression patterns. Our results highlight the utility of an explainable DL approach for identifying novel CRE-TF interactions. These imply that fruit crop species may have evolved lineage-specific fruit ripening-related cis-trans regulatory networks.
先前对许多水果作物品种成熟过程的研究通常涉及对物种间保守遗传因素的分析。然而，即使对于看似相同的成熟过程，相关的基因表达网络往往也是独立进化的，转录因子（TFs）与靶向顺式调控元件（CREs）之间相互作用的多样性就反映了这一点。在本研究中，可解释深度学习（DL）框架被用于基于启动子序列中的CREs预测表达模式。我们最初筛选了影响猕猴桃成熟过程的潜在谱系特异性CRE-TF相互作用，猕猴桃的成熟过程由乙烯触发，这与其他跃变型水果作物的相应过程类似。我们鉴定出了一些影响乙烯诱导果实成熟的新调控关系。具体而言，类似ABI5的bZIP、类似G2的和类似MYB81的TFs被揭示为调节代表性乙烯信号/生物合成相关基因（如ACS1、ERT2和ERF143）表达的反式作用因子。瞬时报告基因检测和DNA亲和纯化测序（DAP-Seq）分析验证了这些CRE-TF相互作用及其调控关系。与共表达网络的比较分析表明，这种基于DL的筛选可以独立于共表达模式识别调控网络。我们的结果突出了可解释DL方法在识别新的CRE-TF相互作用方面的实用性。这些结果表明，水果作物物种可能已经进化出了谱系特异性的果实成熟相关顺式-反式调控网络。
