Genetic improvement of low-lignin poplars: a new strategy based on molecular recognition, chemical reactions and empirical breeding

• Published in: Physiologia plantarum 2025-01, Vol.177 (1), p.e70011
• Main Authors: Duan, Huaichuan, Li, Siyao, Wang, Xin, Ge, Yutong, Song, Yuting, Hu, Dongling, Liu, Wei, Hu, Jianping, Shi, Hubing
• Format: Article
• Language: English
• Subjects: Lignin - metabolism; Methyltransferases - chemistry; Methyltransferases - genetics; Methyltransferases - metabolism; Mutation - genetics; Plant Breeding; Plant Proteins - chemistry; Plant Proteins - genetics; Plant Proteins - metabolism; Populus - genetics; Populus - metabolism; S-Adenosylmethionine - metabolism
• ISSN: 1399-3054; 1399-3054
• DOI: 10.1111/ppl.70011

As an important source of pollution in the papermaking process, the presence of lignin in poplar can seriously affect the quality and process of pulping. During lignin synthesis, Caffeoyl-CoA-O methyltransferase (CCoAOMT), as a specialized catalytic transferase, can effectively regulate the methylation of caffeoyl-coenzyme A (CCoA) to feruloyl-coenzyme A. Targeting CCoAOMT, this study investigated the substrate recognition mechanism and the possible reaction mechanism, the key residues of lignin binding were mutated and the lignin content was validated by deep convolutional neural-network model based on genome-wide prediction (DCNGP). The molecular mechanics results indicate that the binding of S-adenosyl methionine (SAM) and CCoA is sequential, with SAM first binding and inducing inward constriction of the CCoAOMT; then CCoA binds to the pocket, and this process closes the outer channel, preventing contamination by impurities and ensuring that the reaction proceeds. Next, the key residues in the recognition process of SAM (F69 and D91) and CCoA (I40, N170, Y188 and D218) were analyzed, and we identified that K146 as a base catalyst is important for inducing the methylation reaction. Immediately after that, the possible methylation reaction mechanism was deduced by the combination of Restrained Electrostatic Potential (RESP) and Independent Gradient Model (IGM) analysis, focusing on the catalytic center electron cloud density and RESP charge distribution. Finally, the DCNGP results verified that the designed mutant groups were all able to effectively reduce the lignin content and increase the S-lignin content/ G-lignin content ratio, which was beneficial for the subsequent lignin removal. Multifaceted consideration of factors that reduce lignin content and combined deep learning to screen for favorable mutations in target traits provides new ideas for targeted breeding of low-lignin poplars.