ShF5H1 overexpression increases syringyl lignin and improves saccharification in sugarcane leaves.

The agricultural sugarcane residues, bagasse and straws, can be used for second-generation ethanol (2GE) production by the cellulose conversion into glucose (saccharification). However, the lignin content negatively impacts the saccharification process. This polymer is mainly composed of guaiacyl (G), hydroxyphenyl (H), and syringyl (S) units, the latter formed in the ferulate 5-hydroxylase (F5H) branch of the lignin biosynthesis pathway. We have generated transgenic lines overexpressing ShF5H1 under the control of the C4H (cinnamate 4-hydroxylase) rice promoter, which led to a significant increase of up to 160% in the S/G ratio and 63% in the saccharification efficiency in leaves. Nevertheless, the content of lignin was unchanged in this organ. In culms, neither the S/G ratio nor sucrose accumulation was altered, suggesting that ShF5H1 overexpression would not affect first-generation ethanol production. Interestingly, the bagasse showed a significantly higher fiber content. Our results indicate that the tissue-specific manipulation of the biosynthetic branch leading to S unit formation is industrially advantageous and has established a foundation for further studies aiming at refining lignin modifications. Thus, the ShF5H1 overexpression in sugarcane emerges as an efficient strategy to improve 2GE production from straw.

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.