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Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis
- Wang, Jack P;
- Matthews, Megan L;
- Williams, Cranos M;
- Shi, Rui;
- Yang, Chenmin;
- Tunlaya-Anukit, Sermsawat;
- Chen, Hsi-Chuan;
- Li, Quanzi;
- Liu, Jie;
- Lin, Chien-Yuan;
- Naik, Punith;
- Sun, Ying-Hsuan;
- Loziuk, Philip L;
- Yeh, Ting-Feng;
- Kim, Hoon;
- Gjersing, Erica;
- Shollenberger, Todd;
- Shuford, Christopher M;
- Song, Jina;
- Miller, Zachary;
- Huang, Yung-Yun;
- Edmunds, Charles W;
- Liu, Baoguang;
- Sun, Yi;
- Lin, Ying-Chung Jimmy;
- Li, Wei;
- Chen, Hao;
- Peszlen, Ilona;
- Ducoste, Joel J;
- Ralph, John;
- Chang, Hou-Min;
- Muddiman, David C;
- Davis, Mark F;
- Smith, Chris;
- Isik, Fikret;
- Sederoff, Ronald;
- Chiang, Vincent L
- et al.
Published Web Location
https://doi.org/10.1038/s41467-018-03863-zAbstract
A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux, metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.
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